Agent-Based Models
What can you use Agent-Based Models as a technology-to-think-with for?
Many phenomena in both the physical and social world consist not of causal-chains, but of causal-webs, with causal relationships at many different levels and consequent non-linear outcomes. Agent-Based Models are particularly well-suited to think with about such non-linear complex causality, because they allow us to encode a simulation with individual causal behavior at the micro-level, and then observe the resulting systemic causal behavior at the meso- and macro-levels.
What are agent-based models?
Agent-Based Models are a particular type of computer simulation in which you encode behavior into autonomous, interacting parts and simulate the emergent outcomes. This helps see how individual interactions can lead to large-scale change at a systemic level – the so-called “butterfly effect”. Agent-Based Models are used to simulate a wide range of phenomena, ranging from financial markets and consumers, to language shifts and epidemics.
Blocks-Based Agent-Based Models
We also use NetLogo’s new Blocks-Based language, NetTango, to create ABM learning models that do not require any coding experience, but still lets teachers and students build their own models.
What can you use Agent-Based Models for?
As a teacher, you can use Agent-Based Models to better communicate complex phenomena in your discipline, and to support your students in exploring and better understanding complex causality. As a researcher, you can use Agent-Based Models to explore effects of interventions, or to better understand how complex causality can explain phenomena in the physical or social sciences.
Examples of agent-based models
