I thought I’d try something different today, and instead of the regular post, I thought I’d try and do a bite-sized summary of a topic. To that end, please enjoy the first installment of 60 second stats! I can’t install a clock in the post, but if you’re really keen, time yourself to see if I’ve done a good job. Today’s topic will be the area of Agent-Based Simulation.
Are you ready?
….GO!
What is Agent-Based Simulation?
Agent-based simulation is any computer simulation in which “agents” interact with each other.
The agents can be simulated cells in a biological system, or animals in nature, or even people. You then set your simulation running to see how the agents interact.
How do I make an agent-based simulation?
Basically, an agent-based simulation needs 3 elements:
- Agents
- Relationships/interactions between the agents
- An environment the agents exist in
These will often be determined by either the structure of the simulation, or by parameters inputted by the user at the start of the simulation.
Are there any examples of this?
Yes! To pick just a couple of the countless examples, agent based simulation has been used to model:
- Predator-Prey relationships between animals (for example, killer whales and other marine mammals)
- Pandemic disease modelling of individuals
- Prehistoric settlement patterns of early civilisations
And many others.
What do I use an agent-based simulation for?
Often it’s used to either:
- Study how the agents interact under certain conditions, because studying these conditions in real-life is difficult/impossible
- Predict how real-life agents would act in new situations/environments they haven’t experienced before
That seems simple enough. Are there any problems with it?
The main difficulty is calibration. That is, selecting the right parameters so that the simulation is behaving similarly to the real-life system it is modelling. Otherwise, you can’t trust any of the results you obtain from it.
How do you calibrate it?
The most common way seems to be trial-and-error – just keep testing new parameters until the output looks realistic. However, there has been a bigger post to start using heuristics to try and automatically calibrate agent-based simulations
That sounds familiar…
It should! It was the topic of my last blog post!
60 seconds is nearly up. Where do I look if I want to know more about this?
Macal & North (2014) give a really good overview of general agent-based simulation. For some examples for the automatic calibration, see Moon and colleagues (2018) or Calvex and Hutzler (2005).
Aaaand, that’s time. Hope you enjoyed that! If not, well, you only wasted one minute.