Voter model in N-dimensional regular networks (N-dimensional lattice) and complex networks (Albert-Barabasi and Erdos-Renyi)
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Updated
Mar 27, 2020 - Jupyter Notebook
Voter model in N-dimensional regular networks (N-dimensional lattice) and complex networks (Albert-Barabasi and Erdos-Renyi)
Q state Voter model in N-dimensional regular networks (N-dimensional lattice) and complex networks (Albert-Barabasi and Erdos-Renyi).
Code for "Nonlinear, Natural, and Noisy: A Quantitative Approach to the Collection and Analysis of Real-World Social Behavior" (Chiovaro & Paxton, under review).
The purpose was to study the mood of respondents, what are the predictors of mood among students with different personality types and how do these predictors vary between different time diaries.
Simulations of flocking behaviour in active agents, using a simple Viscek model
Continuous-time noisy voter model
Gamified version of a referential communication task described in https://doi.org/10.1044/2018_JSLHR-H-18-0107
The Friction-Flow framework aims to analyze and track narrative field dynamics in complex social systems, emphasizing the evolution, interaction, and influence of stories.
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AI-powered social media simulation framework using LLMs for modeling Twitter dynamics during crisis events. Built for SBP-BRiMS 2024 Twitter Challenge.
Will small variations lead to diverse conversational outcomes? An experiment with conversational agents.
An interactive Jupyter simulator for COSA and studying nonlinear consensus in multi-agent systems.
A multi-agent (LLMs) jury deliberation simulation system.
Multi-modal system analyzing social media, news, art, and music to predict emerging cultural movements and artistic trends years before they mainstream.
This repository contains all the code and resources accompanying the paper titled "Messengers: Breaking Echo Chambers in Collective Opinion Dynamics with Homophily"
Spatial MultiAgent RL for Epidemic Control with Heterogeneous Risk Preferences
Understanding democracy. A place to tinker with computational social science code experiments.
Matplotlib based simulation of crowd dynamics reproducing the "Understanding how a group of people splits" paper.
A stochastic simulation engine for social dynamics. Uses Lanchester Laws and Markov Chains to decode the "dance" of conflict and provide real-time tactical advice (Active/Passive, 3-7-12). Optimized for future AR integration.
Minimal network simulation of text-mediated social self-excitation and synchrony.
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