A Brief Analysis of Web3 Narrative Dissemination Mechanisms Using the SIR Epidemiological Model
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A Brief Analysis of Web3 Narrative Dissemination Mechanisms Using the SIR Epidemiological Model
For a specific Web3 narrative, such as RWA or inscriptions, one can observe and measure its beta and gamma values during narrative propagation to predict whether it can form a long-term, stable consensus.
Navigating Web3 tides with focused insightsAuthor: NingNing
Today, with the help of Microsoft's AI-powered new Bing, I created something cool: analyzing the dissemination mechanism of Web3 narratives using the epidemiological SIR model.
The SIR model is a classic mathematical model in epidemiology and one of the most successful and well-known models for infectious disease spread.
In the SIR model, the entire population is divided into three groups:
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Susceptible (S): individuals who have not yet been infected but lack immunity and are likely to become infected upon contact with infected individuals.
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Infected (I): individuals who are already infected and capable of spreading the infection.
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Recovered (R): individuals who have recovered from infection and gained immunity.
This model not only helps us understand and predict the spread of infectious diseases but also aids in understanding and predicting the diffusion of Web3 narratives.
Those who've read "Narrative Economics" will understand this point well.
End of the科普. Now let's get to the real show:
Step 1: Initialization
Susceptible (S) = Proportion of potential target users for a given Web3 narrative
Infected (I) = Proportion of users already believing in a given Web3 narrative
Recovered (R) = Proportion of users who have become desensitized to a given Web3 narrative
beta = Conversion rate of believing in a given Web3 narrative
gamma = Conversion rate of becoming desensitized to a given Web3 narrative
We set:
S=0.9, I=0.1, R=0.0, beta=0.8, gamma=0.01
Step 2: Generate 10,000 random numbers, import the SIR model from the Scipy library, and process the data using our initialized parameters.
Step 3: Reorganize the data and visualize the Web3 narrative dissemination process using a moving bubble chart.
See the attached illustration for visualization results. Under the above initial conditions, approximately 72% of users will choose to believe in a given Web3 narrative long-term—what the crypto industry commonly refers to as forming a stable "consensus."
Additionally, I tested two other sets of initial conditions:
The first set represents a Web3 narrative with high transmission rate and high desensitization rate, initialized as: S=0.9, I=0.1, R=0.0, beta=0.8, gamma=0.2.
Visualization results show that only 1%–3% of users will choose to believe in this narrative long-term.
The second set represents a Web3 narrative with moderate transmission rate and low desensitization rate, initialized as: S=0.9, I=0.1, R=0.0, beta=0.5, gamma=0.01.
Visualization results show that 62%–76% of users will choose to believe in this narrative long-term.
Conclusion: For any specific Web3 narrative—such as RWA, L2, Web3 gaming, or inscriptions—we can observe and statistically analyze its beta and gamma values during narrative dissemination to predict whether it can form a long-term, stable consensus.
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