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Add rumor spread example with Solara visualization#436

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RodionOm wants to merge 4 commits into
mesa:mainfrom
RodionOm:add-rumor-spread-example
Open

Add rumor spread example with Solara visualization#436
RodionOm wants to merge 4 commits into
mesa:mainfrom
RodionOm:add-rumor-spread-example

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@RodionOm

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-- Summary

This PR adds a small Solara-based rumor spread example to 'mesa-examples'
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The model places agents on a 2D toroidal grid. A configurable number of agents initially know a rumor. Informed agents may spread the rumor to neighboring agents, while a recovery mechanism allows agents to forget the rumor and return to the uninformed state.

-- Features

  • Solara-based interactive visualization
  • Spatial grid view of rumor diffusion
  • Time-series plot of informed agents
  • Parameter controls for:
    • initial spreaders
    • infection strength
    • recovery rate

-- Behavior

The model demonstrates multiple regimes depending on parameter values:

  • Extinction: low infection strength and/or high recovery lead the rumor to die out
  • Equilibrium: balanced infection and recovery produce sustained fluctuations
  • Saturation: high infection strength and low recovery lead to near-total spread

-- Motivation

I built this example while preparing for GSoC and learning Mesa hands-on. I wanted to create a compact, reproducible model that demonstrates parameter-dependent rumor diffusion behavior in a way that is easy to run and inspect.

-- Notes

This model is intentionally simple and designed as an educational example rather than a realistic social diffusion simulator.

@RodionOm

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Hi! Just a quick follow-up — happy to adjust anything or align the example with project guidelines if needed. Thanks!

@dietcoke-17 dietcoke-17 left a comment

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Hey @RodionOm just to let you know that there is already an existing model rumor_mill that covers almost the concepts except the one you introduced the recovery rate. I would rather say could you make changes like network topology or anything or can contribute to the rumor_mill to improve the model

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2 participants