We explain how effective visual messages are created from complex business data. Here you find know-how, tips, examples, and case studies on interactive simulations we have created. It is also a great place to get inspired about new simulation ideas and visual communication dynamics.
Most systems don’t fail, grow, or stabilize because of one clear cause. They evolve through interactions, timing, and accumulation. This post breaks down emergence — how small actions turn into system-level outcomes — and why traditional charts and explanations often miss what actually drives results.
Visual and interactive learning clearly improves understanding—but only when designed right. This article breaks down what research confirms, where it fails, and why more interactivity doesn’t always mean better learning.
Synthetic data in simulations is generated by systems, not collected from reality. This article explains what synthetic data is and what are the methods of synthetic data generation.
A simple explanation of how interactive simulations turn complex ideas into fast, clear understanding—and why they outperform static content.
Not all simulation data is real. This article explains the difference between mock, synthetic, and real data—and how simulations actually generate data instead of relying on it.
Explore the world of Agent-Based Modeling with 37 detailed papers showcasing their analytical summaries and primary sources.
A practical ABM tool landscape: IDEs vs frameworks vs HPC/GPU platforms — plus guidance by use case, GIS needs, and reproducibility.
A simple explanation of why interactive content (like simulations) drives higher engagement and conversions than passive content.
Agent-based models simulate complex systems by modeling individual actors and their interactions. This guide explains how they work with simple examples.
Agent-based modelling explains complex systems through individual behavior and interactions. This guide covers key methods, approaches, and how ABMs are built and used.
Agent-based models explain complex systems by simulating individual behavior and interactions. This guide shows how ABMs work and why they matter.
Viral growth is rarely magic. It’s math, behavior, and timing pretending to be luck. Below are three real client cases where we customized this viral loop simulation to answer concrete business questions. Company names and some details are anonymized, but the problems—and solutions—are very real.
