难度不大，不过没（找到合适的）人做。

一句话版本：场景内的海量二维码识别及激光指向定位。

刷刷数学题，避免老年痴呆。

在youtube上看到这么一道题，觉得有点意思，就记了下来。

方程组如下：

x2 - yz = 2
y2 - xz = 3
z2 - xy = 5

求

x, y, z的值

视频里面也说了有很多解法，但大多比较ugly，推荐了一个：

不失一般性，令
x2 - yz = a ...... (1)
y2 - xz = b ...... (2)
z2 - xy = c ...... (3)

(1)式左右各乘y，(2)式左右各乘z，(3)式左右各乘x
x2y - y2z = ay
y2z - xz2 = bz
z2x - x2y = cx

3个方程加起来，左边均抵消为0
得：
0 = ay + bz + cx

又
(1)式左右各乘z，(2)式左右各乘x，(3)式左右各乘y
相加抵消后得到：
0 = az + bx + cy

两方程可以换成向量点积的表达：
<x, y, z> * <c, a, b> = 0
<x, y, z> * <b, c, a> = 0

如a, b, c均不为零
则<x, y, z> 为与 <c, a, b>和<b, c, a>所在平面垂直的向量

令z = 1，
cx + ay = -b
bx + cy = -a
求得
x = （a2 - bc）/（c2 - ab）
y = （b2 - ac）/（c2 - ab）

因此
x = k（a2 - bc）
y = k（b2 - ac）
z = k（c2 - ab）

代入原方程（1）中求k
k2（a2 - bc）2 - k2（b2 - ac）（c2 - ab） = a

k = 1 /（a3 + b3 + c3 - 3abc）1/2
因此
x = （a2 - bc）/（a3 + b3 + c3 - 3abc）1/2
y = （b2 - ac）/（a3 + b3 + c3 - 3abc）1/2
z = （c2 - ab）/（a3 + b3 + c3 - 3abc）1/2

将2, 3, 5分别代入即可
x = -11 / 701/2
y = -1 / 701/2
z = 19 / 701/2

这道题本身有特殊性，只是向量点积的方式会比较优雅。

周日到香港，本来中移动的手机号是开了漫游的（30元/天，封顶后限流），华为手机给了弹了一个天际通的优惠推荐，9.9元/天，含1G流量。

看着诱人，就点点点，支付完了。然后开通。这时候可能规则上触发了中移动的sim卡保护，要我输入SIM卡的PIN，我也没想起来是0000/1234之类，三次不中，就要输PUK了。

因为在香港，手忙脚乱，也拨不了移动的大陆客服……华为手机这时候输PUK的界面是置顶的，关机也关不了。

实在没办法，就用随身带的nano sim的顶针把SIM卡取了下来。然后手机就又可以用了，天际通上传个证件照，也正常开通网络了。

办完事情后，回到深圳，找到家附近的移动营业厅，也就两三分钟的事情，查到PUK，解锁，然后重置PIN，世界又回复原来的平静。

这么多年看过很多组织内知识分享的形式，流于形式的有之，静静湮灭的也有。

有些文档，从诞生到终止，被打开的次数寥寥可数。

是什么导致了这种浪费。真正有效的经验为何总是存在大脑中。

组织内的知识分为显性知识和隐性知识。显性知识是很容易文档化的，同时也是最容易形式化的，在老成员看来，就是陈词滥调，新成员的角度看，是对自学有用的，然而总是比不上传帮带。

另外，即使是新成员，对所有显性知识的理解能力、接受速度都不一样，快慢的区别在人身上，也在内容上。

强制要去新成员去step by step，对部分人来说，也是一种浪费。

比较好的建议是，组织内部做分享会，新成员根据需要参与。

隐性知识最大的问题是，拥有这些经验和知识的老成员，不能将其显性化，所以，事实上不存在固定的隐性知识的边界，只跟成员的归纳整理和表达能力有关。

培养成员的知识归纳和书写能力，同时创造一个制度将隐性知识显性化，然后参与分享，将缩小对成员个体的隐性知识的依赖。

几种组织内部知识的形式：

文档管理系统——以文档形式管理

内容管理系统——以内容、主题串联知识

内部网络——自由分享知识的IT网络

数据仓库——可用于挖掘新知识

未来百年农民政策建议

卓青

“中国问题的实质是农民问题”，其实，这句话完全应该去掉“问题”这个词，“中国的实质是农民”，这样才稍微合乎一点情理。不过即使是这样，也还远远不足以说明“农民”这个词负担的重荷与它蕴含的无法测度的权柄。应该说，此刻在写下如是言论的时候，我是颇感惶恐的，似乎无论如何得形容都难以真的契合“农民”的身份地位，也就似乎总有大不敬的嫌疑。毕竟，对于执掌中国人生命最核心奥秘的原动力与维系其基本运作的人群来说，似乎根本不是什么可显现的词句能够描述的，在这个意义上，“农民”与其说是个称谓，倒不如说是个无名的名称更为合适。也因此，我总感到怜悯农民是这个世界上最滑稽的事情之一，这就好像救济比尔?盖茨或者启蒙孔子一样无比的荒唐可笑。莫非那些妄图救助农民的人，已经谵妄到自比历代圣王、立法者的程度了？毕竟，也只有这种地位的人才有资格和农民达成某种协议来策动相应的社会变革。

好吧，我还是尽快结束这些无聊的感慨，回到现实一点的问题上，其实即使只通过下面这些相关结构中透露出的一鳞半爪的信息也足可以让我们感受到农民的超然地位和无上权威了。

理解农民的独特地位并不是什么难事，如果我们简略回顾一下中国进入帝国时期以来的城乡经济关联就能看到非常有趣味的景象。比如说，在古代中国据说是十分“重农”的。统治者们清醒地认识到农民，尤其是小农，构成了国家的基层群体，他们作为主体的被统治者所提供的产出满足着城市的需要。因此，国家很在乎农民的生产条件，正常情况下尽量避免干扰农时，也很重视农业生产的顺利进行，地方官员总有鼓励农桑的职责。到这里为止，一切都还正常，农民作为国家的基础力量，通过向上输出剩余品维持国家经济的运作。

可是，下面帝国的统治者似乎过度的“重”农了，重得简直不堪负荷。他们打着农民的旗号，干脆除了农业以外一概不要，轻视城市手工业，打击海外贸易，甚至商业还首当其冲的被重农抑商的口号所压制。问题是这里的农业显然不是什么自给自足的孤立活动，它已然作为城市经济的基础参与到整个国家的运作之中。没有了与其他行业良性互动，如何保证农业自身的良好发展？跨地区的产品交换，农业所需手工业品的提供，都离不开其他行业的同步发展。而现在打击农业外的行业，一方面农业缺少了正常的维持其发展的经济循环，另一方面又把一切征收剩余的重担都压在农民身上，如此重农岂不是对农民的双重打击？——确实很重。如果说这只表明了统治者“太过”重农，那么，接下来，他们的作为就很令人起疑了——他们什么也没做。无论如何，既然农业的剩余品供给是作为整个国家的经济运作基础输入给城市的，那么，城市经济至少应该有所作为才能维持一个基础-上层的结构存在下去。不管是再生产投资，用来拓展新的生产或贸易领域，用来刺激奢侈品的生产与获得，甚至哪怕打打侵略战争呢。

不，他们什么也不做。好像除了维持统治阶级的再生产之外，他们不打算贡献任何的经济功能与运作上的活动了。那么，城市经济在什么意义上仍然和农民的生产处在国家的统一结构之中呢？毕竟，如果统治阶层在经济上无所作为无法提供有效的功能来与农业构成循环互动，甚至还反过来损害农业的良性发展，那么，即使再怎么从政策上注重保护农民，又如何保证农业会继续作为基础提供剩余，确切地说如何保证这个基础-上层的结构能够维持下去？总不见得，来个清静无为就可以把对方纳入到自己的体系中吧，那倒是可以以天下万物为基础了。而想要什么积极的行为也不做，对方就能够源源不断地供给物资，莫不是打劫呢？不过显然，大家都还是帝国的臣民，因此，如此反结构的来参与到农民的供给作为其组成环节的结构运行之中只能表明城市拒绝与农民处在同一个经济体系之中。于是，可以预期的，作为不是基础的基础的农业也就只能容忍整个国家经济的脆弱，稍有些风吹草动，横征暴敛，就要面临崩溃的危机。

而等到了帝国衰微，通过引入西方现代化因素逐渐向共产主义（参阅拙文《共产主义……还没有来？——〈解构的共通体〉与共产主义萌芽（的解构）》）过渡，农民就开始进入一个更加微妙的境遇之中了。从清末开始，为了强调国家正下定决心抵御外侮，维护主权的独立和完整，所有中央政府无一例外的把注意力集中在工业建设尤其是与国家战略安全休戚相关的基础工业上——当然，做得好坏是另一回事儿。从清政府和国民党注重沿海大城市的优先发展，到共产党把重工业建设摆在首要位置，一直到改革之后强调的以工业化、城市化为现代化的主导路线，无一例外的把国家的发展方向集中在了改造旧的农业大国面貌，遵循西方现代化模式上。

这是好事儿啊。首先，农业仅仅作为国民经济的一个必要组成部分了，工业占的比重会逐渐增加；其次，按说，既然城市优先，那么农民就有希望摆脱土地束缚了。可惜的是，似乎中央政府打算证明自己的“重视”永远都是绝对的“重”，就像朝廷之前彻底的重农，现在新型政府彻底的重视工业和城市，重到了似乎忘掉了还有农民这么一回事儿，于是，农民由此开始承担起这“不可承受之轻”。先是清政府到民国时期，国家固然一股脑的重视军事相关产业，而民间资本也只是从农村获得原材料，却并不注重生产与农业互补的工业产品。相反，倒是很多工业建设是奔着和农村手工业竞争市场去的。就好像农业和农村不是什么有机的组成部分，而是个外在的市场与原料来源，结果当然是把农民挤得更加贫穷。而到了共产党时代，干脆改成农业为重工业建设服务。说是服务都还重了，其实是城市拿走了农民的剩余品，而既不注重生产农业生产需要的工业品——从而农民只能高价购买，又不去满足农村市场的需求——轻工、手工产品一样匮乏。总而言之，农业也好，农民也好，都像是消失在了城市化、工业化的现代化体系之中一样，一方面工业生产不去满足农业生产需要，使得后者难以实现自身现代化，另一方面，城市的产品也不去满足农民的需求，双方总也形不成良性互补，农民难以进入产品流通的体系。这真是咄咄怪事，难道说农村不在现代化建设所构成的社会整体分工之内不成？显然这是不可能的，农民还在老老实实地作为提供城市所需的原料、粮食。可是，更加不可能的是，总不见得既不考虑农村市场，又不注重提供农业需要的工业品，就叫做把农村加入现代化体系了吧？难道农村失去了社会分工结构对其的定位，不加入与其他环节的交互就构成了现代社会的有机组成部分了吧？那农村有什么理由，或者作为什么还在继续充当原料的提供者呢？毕竟，无论从国家计划还是现代社会运作的角度，一个组成部分都是在其构成上是社会性的，他的生产、交换都是在其他部分的限定之下的，如果其再生产——工业品、消费品——不在社会的结构规定之内，它的供给也就同样的超出了社会运作的限制。那么，谁又能保证农村的供给仍然在工业、城市的需要之内，不会莫名其妙的脱节了呢——实际上就是多次脱节。

不过这还不是最精彩的，改革之后，局面更是为之一变，市场的发展，工业生产的调整倒是缓解了农村的经济状况。农民也可以较方便的购买到所需的工业品了——虽然价格不见得低，也能享受到城市产出的生活消费品了。而这样的代价是，城市不再仅仅是漠视农村的存在，而是干脆开始抹除农村这种东西了，工业开发带来的耕地大量流失造成了粮食产量的持续下降，同时的农村隐性的大量失业开始浮出水面——确切地说化作了四处漫流的农民工潮。千万别说这是圈地运动，且不说开发占地多大程度上是有价值的置换了本来也应该是工业原料产地的耕地，仅仅说离开土地的农民，他们竟然还是农民。无论如何，工人应该就是工人，可是农民工，好像不论如何失去了土地、离开了乡村，仍然是农民-工。究竟在什么意义上，他们仍然是农民呢？——既不从事农业生产，且大部分时间不处在农村。可他们还就不是城市中的工人，无论从什么意义上。但这样一来，既然农民这个词汇在这里其实已经空洞化了，失去了在社会功能上的实指，那么，继续将这些人作为“农民”定位，除了表明他们不属于城市，也不在社会分工上占据明确的位置之外，还有什么意义呢？于是，到这里，农民似乎成了一个表明无身份的身份标志。在这个意义上被称为农民的话，更像是表示已被社会除名——没有了自己确切从事的行业，不作为功能归属明确的一员加入进“城市”的运作体系，相应的不当然的享有城市体系提供的功能服务，从而流散在城市结构运转的各种角落，不断的出现和消失。再度的，我们遇到了不可思议的景象，一大群人以失去社会功能位置的方式被纳入到社会运作的系统之中，一个没有分工定位的现代社会成员怎么看都像是“方的圆”这种无含义的提法。如果说这样也算作被现代化了，难不成现代社会能够驱使着一群幽灵般的存在为自己劳动？当这里是鬼域，还是赶尸？

到这里，恐怕再没什么能够更好的体现现代化对农村的漠视了，已经把农民转化成了看不见的存在，通过将服从社会分工运作的农村\农民至于功能交互体系之外，使得农民不再作为一种身份定位，农业不再作为一种官能要素。从不是基础的基础到没有身份的身份，农民具备如此超然的地位，殊非幸至。也只有如此，他们才能方便的施展他们的力量，掌控某些最基础的东西，并承担相应的责任。

在经典的帝国叙事之中，农民似乎扮演了一个怪异的终极源头的角色。我们总可以听到这样的讲述：当朝廷衰败，皇帝荒淫暴虐，官员贪腐成风，引得民不聊生，遇上天灾就是饿殍遍野，然后民怨沸腾，终于揭竿而起云云；反过来，若是朝廷兴盛，就是皇帝与民休息，吏治清明，百姓安居乐业等等。概括起来就是民心所向是大势所归，要是统治者怎么怎么勤政爱民就能得位，反过来，要是穷兵黩武、横征暴敛就会失去民心从而垮台。按照这个逻辑，施政原则上要小心一点，不要因为打扰农时，超出农民负担能力，不顾民间情绪而导致激起民变。

看上去是很有道理的。但是，不觉得有什么不对吗？上面所说似乎都是围绕着消极的一面进行的——不是如何过分侵扰百姓，不管是征兵打仗还是赋税过重；就是如何没有侵扰百姓，什么休养生息啊，轻徭薄役啊。好像统治者除了打扰百姓之外没什么事情可做了？统治-被统治关系总还有些积极可作为的事情吧，起码来说，政令作为合法的指令被肯定和执行本身就带有积极的色彩，而不会只是打扰不打扰。等到这个状况翻到农民那一边，可就不是有点儿不对那么简单了。对应于前面的，农民似乎要努力的表现自己是何等的有忍受力，反复的宣称和如此呈现：只要是还没有把他们逼到忍无可忍，或者具体地说，只要没有把老百姓逼得没活路了，他们就不会起来反抗。——这是何等的宽宏与富于忍耐的精神啊！但是，等一等，这话岂不是隐含着，朝廷的所有作为、所有指令，除了是用于被忍受之外再没有什么意义了？你看啊，要是政策好，他们就只是忍受，要是政策不好，他们就不再忍受。反过来掉过去，农民除了忍受之外再没有和朝廷的指令有什么联系了。这个……总不见得政令是邻家的犬吠或者音响，只要关心一下可忍受程度就可以了吧？毕竟，如果政令仅仅是作为偶然打扰一般的东西而与农民发生关系，那么，在什么意义上农民仍然是这个朝廷的被统治者呢？后者发出的指令原则上不再与他们有关联了呀？可是，农民并不是朝廷富于忍让精神的邻人，他们是实实在在的臣民，无论如何都本来应该是朝廷指令的执行者，也就是说，他们本来应该——无论高兴与否——承认指令就是针对他们的，他们有义务执行，而不是好像在给谁面子一样的仅仅在忍受。这里包含着他们自身对此一承担的肯定，这一肯定来自他们的身份角色，否则的话，统治在什么意义上还是统治？岂不是说原则上无关的人，无需承认政令对其自身有着确实合法性的人也一样可以成为正式的执行者，成为被统治者？那他们到底算是服从了还是没有呢？

不过农民显然不在意，或者也许很得意这样的奇妙关系。因为他们随后就宣称，在变乱中，遭罪的总是老百姓，因此希望社会安定。这首先是个虚妄不实的理由，因为显然变乱是社会流动的便捷时期，极端的甚至普通老百姓能够成为皇帝，而旧的上层显然就总体来说会遭受毁灭性的打击，除非农民打算宣称只有遭罪的才叫农民。而如果真的如此，那么农民就成了纯粹的承受侵扰者，也就是说朝廷更替对他们来讲仅仅是是否对他们构成打扰的问题，他们自己则退出了这一变化过程本身——不再肯定结果、从中获利。而相应的稳定的社会也就是不对其构成打扰的，农民对此的肯定就成了，只要不怎么打扰他们，他们就承认其合法性。于是，这进一步扩大上面的悖谬，农民仅仅通过打扰与否关系作为被统治者，就仅仅接受了统治者下达指令-被统治者执行这种结构本身，而对于任何具体的朝廷，具体的指示，都仅仅是些打扰而已。这样一来，由于农民是作为最大规模的、最基底的被统治者，只要他们撤销了实际的对指令的承认，任何层级的政令都会变得飘浮不定，从而随时可能被中断。我们就可以理解，官僚系统为何随时可能扭曲和扰乱皇帝的意志，而农民又可以以此为理由中止对某个朝廷指令的承认，但只要他们仍旧支持统治模式就不会造成结构上的变动，从而帝国必然进入以政令与民心关系为标志的循环更替的局面。

当然，这样做的代价是，为了维持统治者的存在，他们必须承担奉献的义务，以免因为统治-被统治关系的脱节而导致上层的崩溃，而这也就呼应了前面的非基础身份的取得，因为在那里农民不再作为基础处在城乡经济系统中。我们先回顾一个流传了很久的故事，应该说当初编的还是满用心的。传说中，中国古代被称为封建社会，主要的生产关系是地主和佃农之间的租佃关系。地主占有土地，通过收取地租剥削农民，而这就造成了封建社会的主要矛盾，农民抗租也就成了起义的原因。因此，通过打倒地主，重分田地，就解放了农民，结束了封建制度。这个故事运用苏联给出的社会五阶段论，很有力的肯定了共产党的政策合法性，并为其成功构造了一个看上去很合理的原因。

不过就像所有的这种故事一样，它从头到尾都不是真的，正像秦晖先生突出强调的那样，中国古代的农民主要是小农。他们自有耕地，自种自收，直接对朝廷缴纳税赋，而朝廷也重点依靠这种小农来维持自己的收入。而地主-佃农的租佃关系从来都只占少数，甚至有的时候，朝廷还可以免去有功名的大地主的税赋，可见其比例不重。相应的，农民起义也是因为小农受不了朝廷的繁重征敛，放弃土地，造成大量流民而引起。佃农反而对反抗朝廷统治兴趣不大，这也是为什么共产党当年在租佃相对较多的江南终究难以站稳脚跟，最终不得不转战到小农为主的西北去的缘故，当然，那个时候地主和佃户分离也远不是在乡地主那种格局了。可是，这样一来，中国倒确实不是封建社会啦，但更奇怪的事情也就出来了，如果说农民作为佃农当然有理由向地主缴租，那作为自耕农，农民在对谁贡献剩余品呢？又是出于什么理由呢？我们好像不得不在两种形象之间摇摆，一方面是大一统国家与他直接统治的一个一个的农民个体，另一方面则是继续像大地主一样拼命搜刮农民财富的皇帝与朝廷。如果说，共产党的故事至少是合理的，因为它告诉我们朝廷的征敛有着土地的所有与租赁作为合法基础；那么，现在破除了这个神话之后，反而搞不清楚我们习以为常的对农民的剥夺现象究竟如何可能发生的了。如果我们继续按照秦晖先生的看法推论，那么，一个统一国家与他的独立个体的国民之间如何可能发生如此微妙的财富关系？因为后者显然并不对前者负担有贡献财富的当然义务，这又不是对封建主的供奉，如果没有国家明确的原由和规定，这样的征敛看上去无法理解。除非事实上农民仍然对朝廷或者皇帝具有某种财产上的依赖关系，才可能这样没什么说法的直接交上大量地租。

是的，地租，最滑稽的情况就在于这里还是在收地租，官吏们显然仍然按照农民有义务作为农业生产者来负担劳役地租的逻辑进行征敛活动，你种田你就得交粮。而意识形态上，普天之下莫非王土、天子富有四海的道理似乎仍然说得通，于是，作为基层被统治者，农民似乎有着继续向皇帝贡献土地所得的理由。可是，农民到底在什么地方欠着皇帝点儿什么呢？他们有权耕种，很多时候有权买卖土地，租赁土地，甚至晚些时候还买卖租种土地的权利，那么，到底皇帝那里还剩下什么权利在租给他们？这个王土究竟在什么意义上仍然作为一组权利束属于皇帝？要是他能凭空说句这天下土地是我的，就把大家搞成都欠他的了，那岂不是不管什么东西都可以因为一个空头所有权而导致实际所有者欠他租金？要是皇帝所有权如此特别，又什么理由非得按照一般所有权的租赁关系去付他租金呢？于是，在这个空头义务之下，通过支付没有实际对应的权利的租金这种不可能的方式，农民就负担上了向国家-朝廷单方向贡献的义务。就好像他们的私有财产也因为共同作为国家成员而包含了某种亏欠一样。不过这似乎也令真正的地主们处在更尴尬的位置上，他们又在干什么，又是否仍然在仍然能以传统的租佃关系获得地租？假若地主们也一样作为国家成员缴纳莫须有的地租，那么他们乡下征收的合法性难道就不携带着来自朝廷的征敛的肯定？小共同体就不是大共同体的分支吗？

说完古代，翻回头来，我们再看看现代。在逐渐浮出水面的新型社会中，统治-被统治的结构有所变化，而农民的通过弱者身份来定型整个结构这种手段却是一成不变的。其实在民主选举之中，它甚至会体现得更加极端。比如说农村搞基层选举的时候，有些很常见的情况。获胜的很多时候是要有势力，有人脉，村里广布关系网络，亲族众多或者有一群哥们敢作敢为的。而有些突然的权力更替更是要能够发\煽动群众，造出声势来。这样说起来，似乎人多力量大的就能占上风，说好听一点，群众的力量占据主导，说难听一点，就是比谁拳头大。在上访、抗拒上级意见的时候尤能体现这一点，民意如何可不是说着玩儿的。而另一方面，民众的弱势、匮乏利益表达机制、对选举态度冷漠又很常见。经常有人说，选谁都一样——确实一样，因为上台的基本都同等的设法捞钱。而选举又经常受人操纵。因此，相应的说法就是匮乏民主机制，乡村精英形成利益集团云云。

这看上去像是在拼凑一个不可能的形象出来。究竟是人多势众呢，还是精英操纵呢？即使是某些个体煽动带领群体，那么他也就肯定必须得获得群体的肯定，能够为群体争取利益，怎么会真的匮乏利益表达机制？不过如果考虑到占据这个不可能位置的，是一贯无所不能的农民，似乎就不是不可想象的了。首先，他们参与全部所有的权力更替相关行动，从上访群体的组织，公共舆论的制造，到竞选网络的发动，各个竞选圈子的形成，各种与上面的交涉谈判。所有的一切活动，都要得到他们的首肯，都需要争得足够多——越多越好——的人的支持。没有人多是搞不成任何事情的，因为上面的领导要避免引起村民普遍不满导致工作不好开展，不引发众怒是搞不下台上的人的，至于投票，总不见得预先没能控制住多数人就想暗中操作吧。然后，他们下定决心绝对不再以上的这些活动中肯定任何一个实际上受他们支持而成功的人，拒绝把任何在他们推动下上台的人作为他们的意愿的表达途径。方法就是，绝对不让领头的考虑他们自己的意愿。这听上去很滑稽，可事实如此。和上面谈判的时候，究竟达成什么条款，农民们不过问；新班子确定人选，农民们表示不关心，谁当都差不多；构成选举圈子，通过各种关系支持某个核心人物，农民们不表达自己的利益诉求，不去充分的讨价还价。这样一来，显然任何最终的结果、最后的政治利益分配都不是他们的意思，都没有他们的允可和肯定。他们对真正的结果和利益都是保持超然地位的，这与他们无关。这当然绝对是个荒诞的境况，农民们作为决定性力量出现在决策、竞争之中，任何事情离开他们都搞不定，可是他们却又从未支持过任何具体的某个人、某种利益分配方案。那么，究竟他们的决定性作用如何发生的呢？究竟，那些打着群众旗号，靠着群众声势的人，是得到了支持还是没有呢？要是没有，他们无法上台，要说有，农民从未对他们表示肯定。如果说给予对方合法性的支持，使得对方获得竞争的力量筹码，却又并不肯定对方代表自己，不支持对方的决策，这也能够成立的话，哪有什么道理认为被支持的确实是那个人呢？岂不是说不需要农民自己的意愿，也可能获得农民作为群众的力量支持？那这力量算是谁的呢？反正农民不会肯定任何具体的人选。

这似乎恰恰就是农民希望的局面。因为实际上，农民为了充分彻底的表明这种不可能的态度，借用了一切可能的社会关系，他们参与各种小集团，组成各种小圈子，通过亲属、利益、交情、过节等等途径把各种社会力量与联结都网络到了自己提供的支持之中，然后又在每个途径上都宣告自己的无作为与不关心。这样，任何上台的或者正在试图上台的人再别想从农民手上捞到丝毫的确实支撑，所有一切的社会关联都已经被农民纳入到自己的模式之中，不会有任何有效的合法性给予具体的个体或者集团。现在，回过头来看看，一个参与所有具体政治角逐的决定性支持力量从不肯定任何具体的当权者，那么，在这个不可能的局面下唯一真正获得支持的就只有这个上下台模式本身。必须得有一个当权的，因为群众总是会支持或此或彼的人，这个当权的不能不依靠群众力量，因为他也没什么别的力量可以依靠，然而不幸的是，任何当权的又永远不会获得真正针对他的合法性肯定，于是，它唯一的命运就是某一天突然被哄下台。这样，在台面上蹦跶的人们老老实实的按照农民们划好的道儿走，你方唱罢我登场，大家轮流维持这个统治者-被统治者的权力秩序，莫名其妙的上台，突如其来的下台。没有实质的合法性基础，权力的更替也就不可能有什么规则可循，然而上上下下，却又谁都别想逃出这个圈子，真的搅乱这个社会基本的权力框架。这里倒是确实可以说农民们是弱势的，是缺乏表达的，然而也恰恰是这个缺乏利益表达，这个漠然的旁观态度，使得他们有能力控制整个局面，在幕后操纵整个模式，他们的力量就在于他们的弱势。

有一份权力有的时候真的得担一份责任，至少农民很笃定的这样做了。在逐渐开始形成如此强力的控制社会统治形态的状况的时候，他们付出的代价甚至超过了他们的前辈。要想让从不具备确实合法性的政府运转不是容易的事情，它的财富来源显然也不会是什么通常的途径，为此，农民采取了非常极端的办法，并且也由此使得他们作为无身份者加入社会运作不那么突兀。

其实，国民党取代清政府之后，竟然还在那里收地租，就已经很令人惊讶了。因为这可不是富有四海的天子当朝了，这会儿是实实在在的国民政府，是个现代民族国家，它可没有任何哪怕是说法上的权力来征收农民的剩余品，除非他有相当合理的说明。不过这个短期过渡隐含的意思，到了共产党上台之后就明朗化了。这个时候，出了一件非常不可思议的事情，共产党竟然，分田地了。这不是在开玩笑，社会主义国家是生产资料全民所有的，土地不是个人的，也不是政府或者任何实体组织的，而是全体劳动者共同所有的，因此国家代为管理。由于生产资料，这里的土地，并非任何组织或个人私有，因此，不可能将之出租、转卖来获利，否则又将形成私有生产资料构成剥削的局面。实际上，分配给全体劳动者进行劳动生产是没有问题的，原则上，这应该是分配给农民土地的缘故。也就是说，农民并不是作为国家的土地的租种者，他们是作为共同所有者来“共同”使用生产资料进行生产的。请注意，这里的“共同”不是指原则上或者条文上的一起，而是真实发生的现实形态。

这么说，大家应该不会觉得奇怪吧？或者我们复习一下中学课本，依据社会主义国家所秉持的合法性话语，所有的剩余价值都是劳动者创造的，因此都应该归劳动者所有。生产资料私有则会导致持有者不合理的占有部分剩余价值，在资本主义生产关系中，由于其生产方式是集体劳动，产品并不直接由生产者占有，而是由资本家统一获得、销售，再返还必要劳动时间的价值给劳动者，自己占有剩余价值。而在生产资料共有的情况下，也同样必然是集体劳动，产品由社会统一获得然后分配，这样剩余价值是没有被任何人剥夺的仍然回到了劳动者手里。是否注意到了这里面的一个要点？必须要集体劳动，和资本主义生产关系一样，因为只有这样，剩余产品才不是已经直接被劳动者自己获得，而是混在一切无法分割的。也只有这样，社会才有理由把剩余产品统一收走，再统一分配。否则的话，生产资料已经共有，劳动者又本来就应该获得自己的全部劳动所得，有什么理由把已经到他们手上的东西拿走？除非是本人同意进行等价调配。因此，共产党土改之后立即进行合作化是非常正确——当然是法理上正确——的，只有这样，农民是在集体劳动中创造价值，国家才有权力收取产品对其重新进行分配。从这个意义上说，大跃进刮共产风虽然不现实，但逻辑上仍然是合理的，持续地推进集体劳动的规模。同样的，毛指责分产到户是倒退至少在同样意义上是合乎道理的，因为在社会主义情况下继续个体劳动实在是很不可思议的事情。

仔细看一下的话，假设把作为共有生产资料的土地分给每个农民，让他们用——而且还是自己私有的——劳动工具进行农业生产，那么所得产品是谁的？由于是分田单干，农民个体本身就是唯一的劳动者，所有的价值都是他们创造的，从合理上说就应该由他们完全占有。进一步，假如在这个时候仍然从他们手中收取部分剩余产品，由于土地是共有的，因此，这不可能是通过土地出租来获得地租，也就是说这并不是剥削，相应的，提走的那部分剩余品并没有转化成任何私有的财富，而是作为土地这种共有生产资料的产品加入到国家掌握的社会共有财富的再分配之中。到这里，我们应该看到这样做是多么难以想象的事情，明明是农民自己的劳动所得，明明不在社会再分配之中，现在经过这样的转换之后忽然成了社会共有的了。这算什么呢？没有按劳分配，但是也没有剥削，不知马克思于地下当作何感想。需要说明一下的是，这里不是在玩弄意识形态语汇，因为土地作为共有生产资料分配给劳动者，也就是农民使用，并不是个空洞话语，而是事实。农民在原则上仍然是被土地束缚的，倒不是说必须种地，而是说不可能选择不接受土地。农民必须得获得土地，而且还不能原则上任意处置，并且因此脱离乡土进入城市并不轻易，这种种措置的唯一合法性来源显然是土地确实是共有生产资料，农民仍然作为所有者不得不接受这种分配。

当然，假如不提取任何剩余品，那么紧接着问题就变成了为什么如此分配生产资料使得他们直接占有那些产品？由于不是集体劳动，这种分配使用本身就变得难以说明其合理性。而一旦这些产品在加入市场交换，就像眼下这样，那么就无法审定这里面究竟农民有多少成本投入，那价格就成了问题。于是，通过把共有生产资料分给个体使用这种无论如何都无法自圆其说的做法，农民承担了一项艰苦卓绝的责任——把自己的所得作为公共的。这样的慷慨虽然依靠的是他们自身以及农业已经作为非确定的部分加入分工结构，但是，一旦农民带头实现了这种状况，就不等于其他人仍然可以免于这种奇妙的财富转换，因为共有生产资料并非只是土地，于是，可以设想，或者是自己的作为公共的或者是公共的作为自己的。

而在农民赋予的统治权威与贡献的资源基础之上，社会就可以建立他的基本结构。对于帝国来说，就是“以农为本”。就这个词的本意来说，似乎只是想说明，要以农业为根本，注重发展农业，这样才能如何如何。如果说，在战国时期，奖励耕织，通过促进农业生产来积累国家财富，从而可以在战争中保持优势，还确实是把农业作为立国根本，那么到了大一统的时候，把农业作为根本之后怎么样了呢？朝廷优先照顾农业，处处考虑农业生产的便利，朝政清明的时候绝对要优先考虑农民的生产条件，这倒是“本”了，可是“枝干”跑哪里去了？干完了这些之后，朝廷似乎无事可做了，战争也以耗费民力为由不打了，基础建设也因为容易干扰农事不做了，那么把农业作为根本到底是为了什么呢？总不会是为了根本而根本吧？那农业积累岂不是成了其他事情围着转的中心？可是，朝廷又清醒地认识到农民不过是被统治者，只是统治基础，并没有全力支持农业发展之类的。于是，农业就以一个受重视的功能单位充当起了核心的角色，别的事情以之为准绳却又并不把它当目的，那么中心跑到哪里去了？看来在农业的帮助下，国家运转的中心悄然逸出了整个社会功能结构，成了某种不受结构制约的东西，这也就是中央政府本身。他的运转和意志并不受功能结构的制约，它并不需要被某种规定或者规范所支配从而使得其他要素和部门能够围绕他组织，现在农业取代了这个位置，而农业有仅仅是他的根本。于是，它能够在这种社会组织结构之外发号施令，作为真正的中心，超越规则的中心。

相应的，如果在共产主义时期，社会要建立和保持它的结构模式，也就是以官僚系统为代表的无中心、无引导的自身功能膨胀与交织的模态，也同样需要利用农业与农民，同样需要建立围绕农民的基本原则。从形态上看，就应该是“放开”。也就是说，社会治理上放开农民、农业的任何限制，使得他们作为社会组成部分发挥作用的时候没有结构上的限制，作为官能，却又并不遵循官能的规则。其实改革以来一直都在放开，从生产上取消计划，到治理上推进自治，默许农民的外出打工，再到取消对农业的税收。而不难看到，这种放开中，不但农民的行动管制被放开了，连其行动的社会意义也被放开了。你看，生产上取消计划，却没有规定明确的产权分配，没有规定农民、集体究竟有多少权力动员农村资源加入市场；推行自治却连治理的基本框架也一并放开了，没有规定必须有什么样的机构设置与权限分配，甚至没有说明是不是这些都应该自行规定。如此一来，农民有什么理由遵照这种放开行事？确切地说，如何还能按照放开的权限行动，因为这些放开的东西本身的规定和意义也都被放开了。于是，集体可能随时任意处置乡村资源，大家可以靠人多势众争夺利益分配，靠私了来自己维持治安，如此等等。不难看到这种放开才能够保证社会功能并非结构或者中心规定下的部分，而是逸出其外的自身扩展的东西。这条原则也将很好的配合上面的三种社会状况，共同构成完整的新的农民形态。

虽然说上面仍然只是农民外围形态的描绘，而并非正面阐述它的本质，不过仅从这些我们也应该能够感受到农民这个特殊群体散发出来的摄人气息。他的超然地位，他的无上权柄，他的巨大贡献。如果说通常对于社会背后隐藏的支配力量的想象真的有个对应实体的话，那它的最佳人选无疑就是农民。他们通过无身份化隐匿在社会的目光之后，指定基本治理结构，提供活动资源，支持社会运作，就像一个无所不在的阴影一样笼罩在每个人的头上或许是心头上。由此，下面的总结与其说是政策建议，毋宁说是日后新社会形态结构下取悦幕后老大的手段。只是不知道将来究竟谁能中他老人家的意，重新订立盟约。当然，此系危险动作，请勿随意模仿，否则后果自负。

农民政策建议：

政策一：逐步全面放开对农民的限制。首先是流动。在可设想的未来还会逐渐包括结社、集会，自由进出不同行当等。

政策二：始终避免给予农民确定的身份地位。当前是城市的各种服务措施以及鼓励资本大量进入乡村，进一步摧毁乡村结构而又不建立真正的城市。在将来城乡交错加剧后，则会逐渐生成新的身份隔离，比如专业。这应该会逐渐造成旧人群的再农民化，比如就业困难的大学生。从而最终形成新的农民群体，作为社会中匮乏身份定位的人，他们总是流动在各种行业的边缘。

政策三：逐步推进民主化进程。由于目前城市基层的街道仍然是城市性质的，因此农民为主体的民主化尚未有好的增长点。这需要随着城市的再乡村化，也就是说，随着农民的进一步进入与形成，在政区意义上的城市中逐渐形成农民群体和网络，从而仍然以乡村选举的圈子模式作为城市基层选举的基础，并逐渐扩展为国家的民主化。而这也同时将意味着，跨地域不再局限在地理意义上的新城市将会形成。而新乡村的精英则应该是有新城市背景的。

政策四：保持共有生产资料与他对农民的分配。现阶段可以强调农村集体手中没有明确分配的土地等资源的共有，那些资源应该作为社会共有，任何从中获利都必须转化为公共建设投资或福利。将来则可以为新农民提供新的公共资源作为其再生产的条件。

实施的注意事项：

要点一：以上四项政策需要全面协调推进。

要点二：民主化进程要有优先性，原则上应该让经济、社会等政策跟随民主进程的推进来安排。

要点三：应当注意加强心理卫生服务的建设。大力普及心理治疗。

原文链接：https://bitcoinmagazine.com/culture/bitcoin-could-never-be-invented-in-a-university

作者Korok Ray，是德州农工大学梅斯商学院副教授兼梅斯创新研究中心主任。

大意就是当前大学和学院的学术研究主要集中在某一学科的增量中，很难得到一个跨学科的创新，而比特币正是密码学、货币/经济学、网络科学的交叉成就。后面就给了大学发展的一些建议。

Since the announcement of its inception in October 2008, Bitcoin has reached a market capitalization of over $1 trillion. Its growth has drawn both retail and institutional investment, as the financial community now begins to see it as a legitimate store of value and an alternative to traditional assets like gold. Innovations in second-layer settlements like the Lightning Network make it increasingly possible for bitcoin to serve as a medium of exchange.

Yet, Bitcoin has a precarious and somewhat checkered history in academia. Curricula in universities are largely devoid of any mention of Bitcoin. Instead, the teachings are often left to student clubs and nonprofits. Over time this may change, as Bitcoin and the entire cryptocurrency market continues to grow, attracting attention from top talent in both engineering and business. Bitcoin’s absence from university is not a problem with Bitcoin itself, but rather the academy, with its insufficient embrace of innovation, its emphasis on backward-looking data analysis and its excessive preoccupation with individual disciplines rather than collective knowledge. Bitcoin can serve as an inspiration for what academic research can and should be. In fact, it presents a roadmap to change higher education for the better.

Similarities With The Academy

One may wonder why anyone should even assume a relationship between Bitcoin and universities. Technologists are in constant contact with real needs of customers today, while faculty develop basic science that (may) have application far into the future. After all, innovations like Facebook, Microsoft, Apple and even Ethereum were launched by young men who didn’t graduate from college. Yet, it’s no accident Silicon Valley and Route 128 both emerged in proximity to our nation’s greatest coastal universities. So, there’s certainly a correlation between universities and the tech sector. Even so, Bitcoin is different. Bitcoin has an even tighter relationship with its intellectual and academic roots. To understand this, we must peer into Bitcoin’s history.

At the turn of the century, a ragtag band of cryptographers, computer scientists, economists and libertarians — the cypherpunks — exchanged messages over an internet mailing list. This was an obscure electronic gathering of a diverse cadre of scientists, technologists and hobbyists who were developing and sharing ideas of advancements in cryptography and computer science. Here’s where some of the early giants of applied cryptography spent time, like Hal Finney, one of the early pioneers of Pretty Good Privacy (PGP).

It was on this mailing list that the pseudonymous creator of Bitcoin, Satoshi Nakamoto, announced his solution for an electronic payment system. After that announcement, he began to field questions from the forum on both the concept and its execution. Shortly thereafter, Nakamoto provided the full implementation of Bitcoin. This allowed participants of the forum to download the software, run it and test it on their own.

The Bitcoin white paper bears similarity to academic research. It follows the structure of an academic paper, has citations and looks similar to what any paper in computer science may look like today. Both the white paper and the conversations around it reference prior attempts at implementing the proof-of-work algorithm, one of the core features of Bitcoin. For example, the white paper cites HashCash from 2002, also part of the corpus of knowledge that preceded Bitcoin. Adam Back came up with proof-of-work for HashCash while trying to solve the problem of eliminating spam in emails.

Thus, Bitcoin didn’t fall out of the sky, but emerged out of a long lineage of ideas developed over decades, not days or weeks. We tend to think of technology as operating at warp speed, changing rapidly and being driven by ambitious, young college dropouts, but Bitcoin wasn’t based on “move fast and break things.” It was and is the opposite: a slow, careful deliberation based on decades of real science practiced not by kids, but more like their parents. The cryptography forum was similar in nature to an academic research seminar, where professional scientists politely but insistently attempt to tear down ideas to arrive at the truth. Though the concept of a white paper is now all the rage among alternative cryptocurrency coins and tokens, it’s the hallmark method of communicating ideas among the professional research community.

Even though the cryptocurrency economy today occupies center stage in the financial press and a growing share of national attention, when it emerged Bitcoin was as far from this as possible. It was obscure, technical and very fringe. In its long gestation from ideas that had been around for decades but unknown except to a small circle of cryptographers, economists and political philosophers, Bitcoin shares more in common with other radical innovations, like the internet, the transistor and the airplane. Just like those innovations, the story of Bitcoin is the triumph of individual reason over collective misperception. Just as the Wright brothers proved the world wrong by showing man could fly even though physicists claimed it was mathematically impossible, so too did Bitcoin confound the naysayers by building digital scarcity for the first time ever.

Why should we focus on Bitcoin rather than some of the other cryptocurrency tokens, like Ethereum? If you look under the hood, the majority of the innovation of cryptocurrency came from Bitcoin. For example, Ethereum relies on the same elliptic curve as Bitcoin, utilizing the same public key cryptography. Bitcoin emerged over a long gestation period and secret development by a pseudonymous applied cryptographer and was released and debated in an obscure mailing list. For this reason, Bitcoin shares many similarities to the arcane academic circles that occupy modern universities. No professional cryptographer made Ethereum; rather, it was a teenager who even admits he rushed its development. Thus, it’s only Bitcoin that has deep connection to the academy, whereas the more incremental innovations crowding the cryptocurrency space now are more similar to the small advances taken in the modern technology sector.

Differences From The Academy

Bitcoin differs from the academy in important ways. Most significantly, Bitcoin is fundamentally interdisciplinary in a way universities today aren’t. Bitcoin fuses together three separate disciplines: mathematics, computer science and economics. It’s this fusion that gives Bitcoin its power and shatters traditional academic silos.

Public key cryptography has been the major innovation in applied cryptography and mathematics since its conception 50 years ago. The core concept is simple: Users can secure a message with a private key known only to themselves that generates a public key known to all. Therefore, the user can easily distribute the public key without any security consequence, as only the private key can unlock the encryption. Public key cryptography achieves this through hash functions — one-way transformations of data that are impossible to reverse. In Bitcoin, this occurs through elliptic curves over finite fields of prime order.

But public key cryptography isn’t enough. Because Bitcoin seeks to serve as an electronic payment system, it must solve the double-spending problem. If Alice pays Bob using bitcoin, we must prevent Alice from also paying Carol with that same bitcoin. But in the digital world, copying data is free and therefore, preventing double spending is seemingly hopeless. For this, Nakamoto utilized the blockchain, a construct from computer science. Cryptographer David Chaum laid the groundwork for the concept of a blockchain as early as 1983, in research that emerged from his computer science dissertation at Berkeley.

The blockchain is a linked list that points backwards to the original (genesis) block. Each block contains thousands of transactions, each transaction containing the ingredients for transferring bitcoin from one address to another. The blockchain solves the double-spending problem because it’s distributed, i.e., publicly available to all nodes on the Bitcoin network. These nodes constantly validate the blockchain with new transactions added only when all other nodes on the network agree (consensus). In our prior example, when Alice pays Bob, this transaction enters the blockchain, which all nodes observe. If Alice tries to use those same bitcoin to pay Carol, the network will reject that transaction since everyone knows that Alice has already used those bitcoin to pay Bob. It’s the distributed, public nature of the blockchain that prevents double spending, a problem unique to electronic payments.

Indeed, Satoshi designed the blockchain specifically as a solution to double spending. It’s inherently inefficient, as it requires the entire network to constantly validate and reproduce the same data. This is also why most applications of blockchain technology outside of Bitcoin make little sense, as it forces an inefficient solution custom-built for electronic payments onto other applications that would be efficiently solved with central databases. The notion of a blockchain as a reverse-linked list by itself is not revolutionary in computer science, but its distributed nature specifically designed to prevent double spending is.

Even so, cryptography and blockchain aren’t enough. There needs to be a reason for the network to secure the blockchain. This is where the economics of Bitcoin shine. Nakamoto proposed a group of computers that would prove that the history of transactions did in fact occur. This proof requires costly work to be done. Nakamoto solved this by setting up a tournament in which individual computers (called miners) would compete to find a seemingly random answer through a one-way function called SHA256. The winner would receive newly minted bitcoin, which the network would release. The answer to the function must be sufficiently challenging that the only way to solve it is to deploy more computational resources. Bitcoin mining requires real computation and therefore real energy, similar to gold mining a few generations ago. But unlike gold mining, the issuance schedule of new bitcoin is known by everyone.

The economics of mining is the design of a contest that rewards new bitcoin to miners that solve a puzzle. This is a form of a microeconomics mechanism, i.e., a game economy design where individual agents compete for a reward. The macroeconomics of Bitcoin pertains to the issuance schedule, which adjusts predictably over time, with the block reward reducing by half every four years. This forces the constraint of 21 million bitcoin. This inherently limits the inflationary growth of the currency and imposes a constraint no fiat currency today must adhere to. The difficulty of the underlying puzzle adjusts roughly every two weeks regardless of the computing power of the network, providing a robust implementation despite exponential advances in computing power in the decades since Bitcoin launched.

This interdisciplinary feature of Bitcoin is existential, not incremental. Without any of its three components (public key cryptography, a backward-linked blockchain and a mining contest using proof-of-work), Bitcoin would not function. By itself, each of the three components consisted of a coherent body of knowledge and ideas. It was their combination that was Nakamoto’s genius. So too will future radical innovations need to link together multiple disciplines in existential ways, without which their combination would not survive.

Why Not The Academy?

Why could Bitcoin not have emerged out of the academy? First, Bitcoin is inherently interdisciplinary, yet scholars at universities are rewarded for excellence in single domains of knowledge. Bitcoin fuses together ideas from computer science, mathematics and economics, yet it is unlikely any single university faculty would have the breadth of knowledge necessary for interdisciplinary consilience.

Second, the academy suffers from incrementalism. Academic journals explicitly ask their authors for the incremental contribution their work provides to the literature. This is how knowledge advances, inch by inch. But Bitcoin — like other radical innovations in history, such as the airplane and the transistor — made giant leaps forward that would likely not have survived the peer review process of the academy.

Third, Bitcoin rests on libertarian political foundations which are out of favor among the mainstream academy, especially professional economists. Baked into the software are algorithmic representations of sound money, where the Bitcoin protocol releases new bitcoin on a predictable schedule. This is very different from the world we live in today, where the Federal Open Market Committee has full discretionary authority on the money supply. The cypherpunks who vetted Bitcoin v0.1 shared a skepticism of collective authority, believing technology and cryptography can provide privacy to individuals out of the watchful eyes of the government or any large organization.

Most economists don’t share this skepticism towards central authority. At least the social science community never took Bitcoin seriously. Besides, the Federal Reserve has an outsize role in both funding and promoting mainstream academic economic research. It recruits from top Ph.D. programs, hires bank presidents and governors who were former professors of economics, and encourages its staff to publish in the same academic journals as the academy. It is no wonder the university of faculty, influenced by the culture of the Fed, would not embrace technology that radically replaces it.

I asked all living Nobel laureates of economics to speak at the Texas A&M Bitcoin Conference, and all but one declined. Some admitted to not knowing enough about Bitcoin to warrant a lecture; at least they were honest about the constraints of the disciplinary model that they’ve so successfully thrived in. Others, like Paul Krugman, view cryptocurrencies as the new subprime mortgage (he also once predicted that the internet would have the same impact on the economy as the fax machine). Academic economists dedicated almost no attention to Bitcoin’s rise and even now remain ignorant of how the Bitcoin blockchain works, despite being the only real innovation in finance this last decade.

Bitcoin is first and foremost an intellectual contribution. It doesn’t require a deep knowledge of industry, special insight into the current practices of firms or knowledge of idiosyncratic details of the labor and capital markets. It didn’t build from existing practice, but rather from existing theory. For these reasons, Bitcoin emerged unapologetically out of the land of ideas, and should, in some sense, have come from the academy. An academic economist could’ve possibly designed the mining tournament, a computer scientist developed the blockchain and a mathematician developed public key cryptography. It takes an unlikely fellow (or team) to combine these three innovations together. Universities develop faculties with deep expertise in their individual disciplines but do nothing to tie the disciplines together in the way Bitcoin does. For this reason, Bitcoin couldn’t have emerged out of the university, even though it rests on disciplines well established within the university. The problem isn’t the knowledge itself but its organization. And therein lies the opportunity.

How Did We Get Here?

In its current form, the academy is not suited for innovations like Bitcoin. After students enter graduate school, they learn the techniques of their own discipline, which they use to publish in specialized journals that earn them tenure and future academic recognition with a small set of peers within that discipline. These isolated corridors of knowledge have ossified over centuries ever since the early universities. How did this happen?

There are two primary trends in the academy since World War II. By far the most important is the digital revolution. As computing power became accessible to anyone, the objective of science shifted from building theory to measurement. Suddenly, a wide array of social and natural science data was available to researchers from a laptop anywhere in the world. The growth of the internet spread data sharing and data availability, and advances in microprocessing power made large analysis of data cheap and easy. The academic community shifted en masse to data analysis and moved from trend to trend on 10-15 year cycles. The first cycle was on summary statistics and variance analysis, the second was on linear regression and the third on machine learning. When problems arose in the specific domain of each discipline, rarely did scholars return to their underlying theory for revision. Instead, they simply fed more data into the machine, hoping measurement error and omitted variables were to blame.

The growth of big data and statistics, in concert with machine learning, has led us to the present where artificial intelligence (AI) is a black box. No researcher can fully explain what exactly AI is doing. At the same time, questions have become smaller. Before, development economics as a field would ask, “Why is Africa so poor?” Now, research in the field asks whether placing a sign on the left or the right side of a bathroom door is more likely to lead to usage. This preoccupation with causality is intellectually worthwhile but comes at a high price, as often the researcher must narrow his domain to behaviors that are easily observable and measurable. The large, complex and mathematical theories developed after World War II were largely untestable, and so empirical researchers abandoned those theoretical foundations. Where once academics held the intellectual high ground by asking the biggest questions of the day, now empirical research dominates academic journals. Experimental physicists and empirical economists alike mostly cite other data-driven work.

As computers filtered throughout our society, students had exposure to computation earlier in their lives. By the time they arrived in college and in graduate school, they already had basic facilities with data manipulation and analysis. Why bother with mathematics when some simple experiments and linear regressions can provide tables of results that can be quickly published? Over time, students gravitated towards data work as the academic profession slowly migrated away from math.

It became far easier for journals to accept papers with some small experimental or empirical fact about the world. Given that editors and referees make decisions on academic research on a paper-by-paper basis, there’s no overarching evaluation of whether the body of empirical and experimental work truly advances human knowledge. As such, data analysis has run amuck with teams of researchers making ever more incremental advances, mining the same core data sets, and asking smaller and more meaningless questions. Does rain or sunshine affect the mood of traders and therefore their stock picks? Can the size of a CFO’s signature on an annual statement measure his narcissism and predict if he will commit fraud? (I’m not making this stuff up.)

One might think that advances in computation would have led research to verify some of the theories developed after World War II, but that has not been the case. In technical terms, many of those complex models are endogenous, with multiple variables determined in equilibrium simultaneously. As such, it’s a challenge for empirical researchers to identify specifically what’s happening, such as whether increasing the minimum wage will increase unemployment, as Economics 101 suggests. That has led to a turn to causality. But causal inference requires precise conditions, and often those conditions do not hold over the economy but rather in a few specific examples, like U.S. states that adopted anti-abortion laws at different times. The Freakonomics revolution in economics may not dominate the Nobel Prizes, but certainly has influenced the majority of published social science research.

The chief problem with this data-driven approach is its ultimately backward-looking approach. By definition, data is a representation of the world at a point in time. The entire fields of business and economics research are now almost wholly empirical, where scholars race to either gather new datasets or use novel and empirical techniques on existing datasets. Either way, the view is always from the rearview mirror, looking back into the past to understand what did or didn’t happen. Did low interest rates cause the Global Financial Crisis? Do abortions reduce crime? Does the minimum wage reduce employment? These questions are fundamentally preoccupied with the past, rather than designing new solutions for the future.

The second trend has been the shrinking of the theory community, both inside and outside the academy. The number of theorists has vastly shrunk, and they also have refused to collaborate with their much larger empirical and experimental colleagues. This tribalism led theorists to write ever more complex, intricate and self-referential mathematical models with little basis in reality and no hope for possible empirical validation. Much of game theory remains untestable, and string theory is perhaps the most extreme example of a self-referential world that can never be fully verified or tested.

Finally, academic theory trails technology by a long time. Often, mathematicians, physicists and economists provide ex-post rationalizations of technologies that have already been successful in industry. These theories don’t predict anything new, but rather simply affirm conventional wisdom. As the complexity of theory grows, its readership falls, even among theorists. Just like everything else in life, the tribalism of theory leads the community to act as a club, barring members who don’t adopt its arcane language and methods.

Thus, we’ve arrived at something of a civil war; the theory tribe is shrinking year by year and losing relevance to reality, while the empirical/experimental data community grows over time, asking smaller questions with no conceptual guidance. Both academics and technologists are left in the dark about what problems to solve and how to approach them. It also leads to a pervasive randomness in our collective consciousness, leading us to blow in whatever direction the winds of the moment take us. Economics has well-established theories of markets and how they function, yet technology companies are massive marketplaces unmoored in much of that same economic theory. Computer science rests on a sturdy foundation of algorithms and data structures, yet the theory community is obsessed with debates on computational complexity, while trillion-dollar tech companies perform simple A/B tests to make their most significant decisions.

We’ve reached a tipping point in the scale of human knowledge, where scholars refine their theories to ever more precise levels, speaking to smaller and smaller communities of scholars. This specialization of knowledge has led to hyperspecialization, where journals and academic disciplines continue to divide and subdivide into ever smaller categories. The profusion of journals is evidence of this hyperspecialization.

From Science To Engineering

Much future innovation will occur at the boundaries of the disciplines, given that much knowledge has already been discovered within existing disciplines, but there must be a greater transformation. Universities today still largely adopt the scientific method, establishing knowledge for its own sake and seeking to know the natural, physical and social world, but we shouldn’t stop there. Given their fundamental knowledge, scientists are in the best position to engineer better solutions for our future. Moving to an engineering mindset will force academics to design and implement solutions to our most pressing problems. In the long term, it will also close the gap between the academy and industry. The pressure students face to search for jobs and start companies, which takes a toll on their academic coursework, emerges because there’s a gap between the needs of the market and the academic curriculum. Were this gap to close, and students instead spent time in college building better solutions for the future, this cognitive dissonance would dissipate.

This transformation has already begun in some disciplines, like economics. One of the most successful applied areas of economics is market design, which unambiguously adopted an engineering mindset and delivered three Nobel Prizes in the last decade alone. These scholars came from engineering and adapted game theory to build better markets that can work in the real world, such as better ways to match kidney donors to recipients, students to schools or medical residents to hospitals. They also designed many of the largest auctions in use today, such as the spectrum auction of the government and the ad auction within Google. There’s no reason the rest of the economics profession, or even the rest of higher education and the academic community, cannot similarly position themselves towards adopting more of this engineering mindset.

Over time, closing this gap between the academy and industry will relieve much of the  
public outcry against escalating tuition and student debt. Once students and professors orient their research to develop better solutions for society, so too will their students and the companies that employ them. Students will no longer resent their faculty for spending time on research rather than teaching if that research directly creates technologies that ultimately benefit the students, future employers and society at large. Over time, this naturally will close the skills gap that America currently faces. Universities no longer will need to focus on STEM skills explicitly, but rather focus on providing technological solutions that will ultimately draw heavily from the STEM areas anyway.

A Call To Action

How can we reform higher education to produce the next Bitcoin? Of course, the next Bitcoin won’t be Bitcoin per se, but rather a first-principled innovation that conceives of an old problem in an entirely new way. I have three specific recommendations for university culture, priorities and organizational structure.

First, the academy must more explicitly embrace engineering more than science — even on the margin. The Renaissance and the Age of Reason have led American higher education to celebrate science and knowledge for its own sake. The motto for Harvard is “Veritas,” or “truth,” while that of the University of Chicago is “Crescat scientia, vita excolatur,” meaning “Let knowledge grow from more to more, and so human life be enriched.” These universities, based on the scientific and liberal arts traditions, have done much to establish the corpus of knowledge necessary for human progress, but this last half-century has been the age of the engineering universities, with Stanford and MIT competing to build solutions for the world, not just to understand it. This ethos of engineering should extend beyond engineering departments, but even and especially, to social science. For example, require all freshmen to take a basic engineering class to learn the mental framework of building solutions to problems. Economists have articulated the benefits of sound money for generations, but only through an engineered system like Bitcoin can those debates become reality.

This shift in engineering is happening somewhat within the social sciences. For example, the recent Nobel Prizes given to Paul Milgrom and Bob Wilson in economics celebrated their work in designing new markets and auctions to solve real problems in resource allocation problems that governments and society face. This community of microeconomic theorists are still a small minority within the economic profession, yet their work blends theory and practice like no other field and should have higher representation among practicing scholars. Universities should abandon the forced equity in treating all disciplines as equal, allocating an even share of faculty lines and research dollars to every discipline, no matter its impact on society. Instead, prioritize disciples willing and able to build solutions for the future. This culture must come from the top and permeate down towards recruiting decisions of faculty and students.

Second, reward interdisciplinary work. The traditional, centuries-old model of deep disciplinary work is showing its age, while most of the exciting innovations of our time lie at the boundaries of the disciplines. Universities pay lip service to interdisciplinary work as a new buzzword across college campuses, but unless the incentives for faculty change, nothing will. Promotion and tenure committees must reward publications outside of a scholar’s home discipline and especially collaborations with other departments and colleges. While large government agencies, like the National Science Foundation, have increased allocation of funding toward cross-disciplinary teams, when it comes times to promotion and tenure decisions, faculty committees are woefully old-fashioned and still reward scholars within rather than across disciplines. Over time, I expect this to change as the older generation retires, but the most pressing problems of society cannot wait and universities should pivot faster now. Unless promotion and tenure committees explicitly announce recognition for interdisciplinary work, nothing else matters.

Third, the academy must aim high. Too often, academic journals are comfortable seeking incremental contributions to the fund of knowledge. Our obsession with citations and small improvements inevitably leads to small steps forward. Academic communities have a reflexive desire to be self-referential and tribal. Therefore, scholars like small conferences of like-minded peers. Some of the biggest steps forward in the history of science came from giant leaps of understanding that only could have occurred outside of the mainstream. Bitcoin is one example, but not the only one. Consider the discovery of the double helix, the invention of the airplane, the creation of the internet and more recently the discovery of the mRNA sequence for the COVID-19 vaccine. True progress comes from unapologetically tossing out the existing intellectual orthodoxy and embracing an entirely fresh look. The standards of excellence for our faculty and students must insist they aim to solve the biggest problems facing humanity. Too often this discourse is silenced from campus, and over time, it erodes the spirit of our young people. To achieve this, allocate research funding based on impact and make these requirements strict.

The vast increase in wealth from the technology sector has put various pressures on campus. For one, it induces young students to drop out and start new companies, following in the footsteps of the young founders who dominate the technological and financial press. This happens only because there’s a rift between the rewards of the market and the activities of the university. Remember that Bitcoin emerged from a small community of intellectuals seeking to engineer a solution to an ancient problem using new technology. This could’ve easily occurred within the academy, and in some sense, it should have.

The corporate firm, either start-up or established, is the natural locus for incremental innovation. The constant noise of customer needs, investor demands and industry knowledge make it a natural place for small changes in society’s production possibilities. Radical innovation is uniquely suited to the academy with its longer, more deliberate timescale, access to deep science and isolation from the noise of the market, but it’s up to the academy to rise to that challenge. Let Bitcoin inspire us, so the academy becomes the quarterback and not just the spectator to the next radical innovation of our time.

看到一篇批判元宇宙的文章，metaverse == metaworse？

对元宇宙的质疑来源于，为了让人群更容易理解和使用元宇宙，使用了过度的拟物化，比如元宇宙银行，就真的搞个虚拟网点，虚拟客户经理，虚拟窗口，问题是只是方便了理解，容易学习，但却阻碍了真正的进化。

过度拟物化也带来元宇宙的能耗虚高。

想起一个例子，新生代对保存/Save的图标的理解就是一个方方的的形状，不知所起。因为软盘在90年代末期就开始消失了……当时windows软件的设计为了让80年代的使用者理解文件被保存下来，使用了保存到软盘的概念来指代Save，然后就是陈陈相因，直到今天。

元宇宙的几个支撑成功的关键点：

有内容创作者——这个跟WEB 2.0是一样的，只是3.0里面是去中心的，创作者完全拥有作品的所有权。

完善的数据经济——作品归属后可以自由交易，其他非展现型的数据也是一样。

加密货币——这是支撑去中心化的基础。

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