用流行病学模型SIR简析Web3叙事的传播机制
作者：NingNing，EMC_Labs 高级研究员 来源：X，@0xNing0x
今天在微软AI工具新Bing的帮助下，做出了一个酷东西：基于流行病学模型SIR分析Web3叙事的传播机制。
SIR模型是流行病学中一个经典的数学模型，是最成功、最著名的传染病传播模型之一。
在SIR模型中，全体人口被划分成三类人群：
- 易感人群（S）：尚未被传染的人群，但缺乏免疫能力，与感染者接触后容易受到感染。
- 感染人群（I）：已经被感染并具有传播力的患者群体。
- 康复人群（R）：从感染中恢复并且取得免疫的人群。
这个模型不但可以帮助我们理解和预测传染病的传播过程，也可以帮助我们理解和预测Web3叙事的传播过程。
关于这点，读过《叙事经济学》的朋友们都懂的。
科普结束，下面我们开始真正的表演：
第一步：初始化条件
易感人群（S）= 某Web3叙事的潜在目标用户比例
感染人群（I）= 已相信某Web3叙事的用户比例
康复人群（R）= 已脱敏某Web3叙事的用户比例
beta = 相信某Web3叙事的转化率
gamma = 脱敏某Web3叙事的转化率
我们设定：
S=0.9，I=0.1，R=0.0，beta=0.8，gamma=0.01
第二步：生成10000个随机数，从Scipy库导入SIR模型，再传入我们的初始化参数处理数据。**
第三步：重整数据，使用移动气泡图可视化Web3叙事传播过程。**
可视化结果见附图，在以上初始化条件下，~72%的用户会选择长期相信某Web3叙事，即加密行业常说的形成稳定“共识”。
此外，我还测试了另外两组初始化条件：
第一组的Web3叙事特性是高传播率、高脱敏率，初始化条件为：S=0.9，I=0.1，R=0.0，beta=0.8，gamma=0.2。
可视化结果显示，仅1%~3%用户会选择长期相信这一组Web3叙事。
第二组的Web3叙事特性是中等传播率、低脱敏率，初始化条件为：S=0.9，I=0.1，R=0.0，beta=0.5，gamma=0.01。
可视化结果显示，会有62%~76%用户会选择长期相信这一组Web3叙事。
结论
对于某特定的Web3叙事，如RWA、L2、Web3游戏、铭文等，我们可以观察和统计其叙事传播中的beta值和gamma值，预测其能否形成长期稳定的共识。
The author, a senior researcher, made a cool thing today with the help of Microsoft Tools. The transmission mechanism model based on epidemiological model analysis and narration is a classic mathematical model in epidemiology, and it is one of the most successful and famous infectious disease transmission models. In the model, the whole population is divided into three groups: the susceptible group, the uninfected group, but the lack of immune ability. After contacting with infected people, it is easy to be infected, and the infected group has the ability to recover. This model can not only help us understand and predict the spread process of infectious diseases, but also help us understand and predict the spread process of narratives. At this point, the popular science that friends who have read narrative economics know is over. Let's start the real performance. The first step is to initialize the conditions of susceptible people. The proportion of potential target users of a narrative is the proportion of infected people who have believed in a narrative. The proportion of users who have recovered from a narrative is desensitized to believe in a narrative. The conversion rate of things desensitizes to the conversion rate of a narrative. We set the second step to generate a random number from the library and then import it into our initialization parameters to process the data. The third step is to reorganize the data and use the moving bubble chart to visualize the narrative communication process. See the attached figure for the visual results. Under the above initialization conditions, users will choose to believe in a narrative for a long time, which is often said in the encryption industry to form a stable consensus. In addition, I also tested two other initialization conditions. The narrative characteristics of the first group are high communication rate and high desensitization rate initialization. The condition is that the visual results show that only users will choose to believe this group of narratives for a long time, and the narrative characteristics of the second group are medium communication rate and low desensitization rate. The initialization condition is that the visual results show that users will choose to believe this group of narrative conclusions for a long time. For a specific narrative, such as game inscriptions, we can observe and count the values in its narrative communication and predict whether it can form a long-term and stable consensus. 比特币今日价格行情网_okx交易所app_永续合约_比特币怎么买卖交易_虚拟币交易所平台
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