Before we get started
The goal of this article is to quickly familiarise yourself with some of the upcoming vocabulary. This includes vocabulary, models and reasons why we want to make agent based models. Let’s start with some simple questions:
What is a model?
This question is more difficult to answer than you might expect. But let’s say a model is a set of concepts that represent an idea. It is a simplification of reality.
Why do scientists model?
Models can serve different purposes depending on the user. We have several different reasons to model complex systems.
- To improve our understanding of certain (emergent) phenomena.
- To make mental models explicit so that it, and its assumptions, are open for all to see and discuss.
- To better understand / predict possible consequences of our decisions.
What is a computer model?
Let’s say a computer model is a set of concepts that represent an idea written in 1’s and 0’s in a computer. We can simulate our idea by using a computer simulation.
What is an agent based model?
An agent-based model is a specific class of computer models where we model the interactions of autonomous agents.
What is an agent?
In an agent-based model an agent is an individual decision maker.
How is this relevant for this course?
Agent based modelling is a tool that we can use to get a better understanding of complex systems. We can use it to study how emergent phenomena come about from the interactions of individual agents.
What is the best way to understand agent based models?
The best ways are to create or interact with them. This is an introductory course, which means that we will only look at a few examples of agent based models. We have prepared material that will allow you to interact with some models yourself. If you want to go even further, please do, we will provide some links that can help you on your way to make your own agent based model.
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