科学与数学meltingasphalt.com编辑精选
走向临界
Going Critical
2019 — 2022，它一次次回到首页
黑点是上首页（≥ 50 分）的那次投稿，空心点是没冲上去的投稿，点越大分数越高。
导读
这篇交互式长文借助可操作的网络模拟，讲解信息或观念如何在网络中传播，以及为什么存在临界点：要么扩散开来，要么逐渐消亡。它重点讨论网络结构（如节点度数）如何改变临界阈值，并延伸到社交网络、城市密度等现实场景，读者可以亲手调整参数观察传播结果。
为什么经久不衰：它把抽象的传播临界性变成可亲手调整的模拟，让人直观看到网络结构如何决定观念兴衰，因此多年后仍能帮助理解社交媒体、疫情和信息扩散。
HN 评论精选
交互式模拟直观展示节点度数如何大幅降低传播临界阈值。
这篇文章感觉像即时经典。它把散文和代码巧妙结合，用来讲授关于网络的高度相关课程。一个开眼界的点是“度数”参数在多大程度上降低临界阈值。节点度数是邻居数量；之前看度数4的网络，变化会怎样？交互文章的力量在于你能自己试。玩具模型却有直观说服力，像我们曾梦想的万维网媒体。社交网络让度数参数爆炸，大大降低思想传播的临界阈值；我们的文化DNA正以超临界速度被改写。这篇帮助稍微理解这一点，静态文字做不到。
hirundo查看英文原文
This essay feels like an instant classic. It's a very thoughtful blend of prose and code, in service of teaching some wildly relevant lessons about networks. One eye opener is the extent that the "Degree" parameter reduces the critical threshold. > The degree of a node is the number of neighbors it has. Up to this point, we’ve been looking at networks of degree 4. But what happens when we vary this parameter? And here's the power of this interactive essay, you can try it yourself. It's a toy model but makes a visceral argument. It adds up to the kind of media we dreamed of for the world wide web. The degree parameter has exploded via social networks, greatly lowering the critical threshold of idea transmission. Our cultural DNA is being revised at a supercritical pace. This piece helps make a little sense of it in a way that static words couldn't.
传播模型应处理过时或死亡信息，需要墓碑机制防止复发。
早期影响我分布式系统思考的论文之一是 Xerox 的 Grapevine，其分布式命名服务用病毒模型传播更新。最有趣的是传播“死亡”信息。文章里东西要么活要么死。可扩展成多种信息（蓝红绿点），你想“杀死绿色”使绿色不再有效。网络需要保留 tombstone（流行病学里的残余抗体）来压制已死信息的复发（当长时间下线的节点重新加入时可能发生）。这是很好的编程练习。
ChuckMcM查看英文原文
One of the more influential papers on my early thinking of distributed systems was the Xerox paper on Grapevine, their distributed naming service that used a viral model for propagating updates across their networks. The most interesting part was transmitting 'dead' information. In the linked article things are alive or not. Now expand this article so that you have a series of things that are being transmitted (maybe blue, red, and green dots to represent each). And then you want to 'kill green' so that green is no longer considered a valid state. Networks need to retain the notion of a tombstone (in epidemiology a residual antibody) to quash flare ups of previously killed information (which can happen when a node that has been down for a while rejoins the network). It makes for some great coding exercises!
临界阈值很陡峭，语言里缺一个词可能决定观念能否扎根文化。
可以用临界性来修正萨丕尔-沃尔夫假设。强版本“语言决定思想”不符合日常经验；但如果母语缺少恰当的词，表达会更费力，需用更多词组合出精确含义。文章问SIS网络的临界阈值；关键不是数值，而是阈值相当陡峭。两个观念传播难度都接近阈值时，一个略高于阈值就成为文化常备，一个略低于阈值则偶现即灭。因此弱版本的沃夫式主张需要考虑网络临界性。
alan-crowe查看英文原文
You can use the concept of criticality to revise and revive the Sapir-Whorf hypothesis. In the blunt form: language limits what we can say; Sapir-Whorf runs contrary to every-day experience. Sure, if ones native language contains le mos juste, it is easy to speak ones mind. But if not, the burden is not great. One must speak at greater length, using more words, and forming the intersections and unions of their meanings, to obtain the exact nuance that you intend. This is the routine craftsmanship of every wordsmith. Early in the essay Kevin Simler posses a challenge "Here's an SIS network to play around with. Can you find its critical threshold?" What is most interesting is not the numerical value, lets just call it x. What is most interesting is that it is fairly sharply defined. If two idea are both fairly hard to transmit, and hence both close to x, we could easily have a situation in which the burden imposed by a missing mos juste makes all the difference. One idea has a transmissibility just above x and becomes an established staple of the culture. The other idea has a transmissibility just below x so it crops up from time to time but always dies out. One looks around, admiring the cultural landscape. One idea is present, one absent. Why? Language! While it is wrong to claim that "a person's native language determines how he or she thinks", we have to take account of network criticality. The much weakened Whorf-style claim that "a person's native language burdens their communications with trivial inconveniences" is plausible and unimportant at the individual level. But we may never-the-less find that "a social network's native language determines which thoughts die out and which ones take over most of the network." Compare and contrast with Beware Trivial Inconveniences https://www.lesswrong.com/posts/reitXJgJXFzKpdKyd/beware-trivial-inconveniences, which claims that trivial inconviences have real world potency without needing the leverage provided by network effects.
可扩展方向包括真实网络拓扑、平台传播差异、审查过滤和长距离连接。
好文章，交互图很好解释概念。我能想到扩展：用全球光纤网络和本地扩展更真实，比较P2P分享与发到Twitter等“中央权威”的信息传播；有研究显示假信息在Twitter上比准确信息传播更快，归因于“惊讶因素”。还可加入威权过滤效应（如中国防火墙如何影响信息传播）。就这个简单最近邻模型而言，复杂因素是从节点到远节点的长距离传输（如新冠经飞机快速传播）。文章确实表明基于图的网络思维是处理这些问题的好方法。
photochemsyn查看英文原文
Great article and the interactive graphs work well to explain concepts. I can think of a few extensions: make it more realistic with a global map of fiber-optic cable networks and local extensions, and then compare how information spreads through things like peer-to-peer sharing vs. information posted to 'central authorities' like Twitter. (Notably there was also that study that showed false information spread faster across Twitter than accurate information, attributed to the 'surprise factor'). This could also be modified to include the authoritarian filter effect (i.e. how does China's 'Great Firewall' affect the spread of information, for example). With respect to this simple nearest-neighbor model, I suppose a complicating factor would be long-distant transport from a given node to a distant node by some out-of-plane connection method (i.e. Covid spread rapidly by airplane, for example). The article really shows that graph-based network thinking is a great way of approaching these problems, nice work.
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