Narrative Economics - SIR Model

Interactive visualization of how economic narratives spread like epidemics using the SIR model

Parameters

Basic Reproduction Number (R₀)
2.50
High epidemic potential
Susceptible (S)
Infected (I)
Recovered (R)

Phase Plane Analysis

Click on the phase plane to set initial conditions

Current S: -
Current I: -

Scenario Explorer

Viral Spread

High contagion, low recovery (R₀ > 1)

c = 0.8, r = 0.2 R₀ = 4.0

Quick Fade

Low contagion, high recovery (R₀ < 1)

c = 0.1, r = 0.5 R₀ = 0.2

Sustained

Moderate both rates (R₀ ≈ 1)

c = 0.3, r = 0.3 R₀ = 1.0

Recurrent Narrative

SIRS model with periodic resurgence

c = 0.5, r = 0.2, δ = 0.05 R₀ = 2.5

Scenario Comparison

Historical Narrative Epidemics

1970s-1980s

The Laffer Curve Narrative

The "supply-side economics" narrative that tax cuts could increase government revenue spread rapidly through economic circles, eventually influencing Reagan-era policy decisions.

Peak Infection: ~1980
Duration: ~10 years
2007-2009

Great Recession Narrative

The "Great Depression" narrative resurged during 2008-2009, influencing policy responses and public behavior. The comparison to 1929 amplified fear and changed consumption patterns.

Peak Infection: Late 2008
Duration: ~2 years
2017

Bitcoin "Get Rich Quick" Narrative

The "decentralized wealth" and "digital gold" narratives spread virally through social media, driving explosive price growth followed by eventual narrative fatigue and price correction.

Peak Infection: Dec 2017
Duration: ~6 months

Narrative Pattern Analysis

Mathematical Foundation

The SIR Model

dS/dt = -c · S · I

dI/dt = c · S · I - r · I

dR/dt = r · I

Where S (Susceptible) represents people who haven't heard the narrative, I (Infected) represents active spreaders, and R (Recovered) represents those who have lost interest or become immune to the narrative.

Basic Reproduction Number (R₀)

R₀ = c / r

  • R₀ > 1: Narrative grows exponentially (epidemic)
  • R₀ < 1: Narrative dies out
  • R₀ = 1: Critical threshold

Narrative Mechanics

Contagion Parameter (c)

How quickly people who hear the narrative start spreading it. Influenced by:

  • Emotional resonance
  • Simplicity and memorability
  • Social media amplification
  • Authority figures endorsing
Recovery Parameter (r)

How quickly people stop spreading the narrative. Influenced by:

  • Attention span and novelty decay
  • Competing narratives
  • Counter-evidence
  • Narrative fatigue

Social Media Impact

Modern social platforms dramatically increase the contagion parameter (c) through:

  • Network effects and viral sharing
  • Algorithmic amplification
  • Echo chambers and filter bubbles
  • Influencer economics

Policy Applications

Understanding narrative epidemics helps in:

  • Predicting market bubbles and crashes
  • Designing effective economic communications
  • Countering harmful economic narratives
  • Anticipating policy resistance