Modelling
[outline]
In this part we'll look less at Python, and more generally at the principles behind modelling the world, introducing some of the background to elements we coded in the Core course. Note that the final slides on the modelling process are quite extensive, so leave plenty of time for them! There's also a handout with a couple of exercises to work through: see below.
First, an introduction to modelling.
modelling basics (powerpoint)
Further info:
Di Paolo, E. A., Noble, J. and Bullock, S. (2000) Simulation models as opaque thought experiments In: Seventh International Conference on Artificial Life, pp. 497-506, MIT Press, Cambridge, MA.
Next, two example models: Cellular Automaton and Agent Based Models.
Further info:
Handout for this lecture - work through this when indicated in the slide notes; there are two parts.
Conway's Game of Life, with animations (Wikipedia)
Playable version with creatures
CA forest fire animation
CAs used in bush/forest fire research
CA and ABM (powerpoint)
Finally, let's look at the modelling process.
The modelling process (powerpoint)
Further info:
List of cognitive biases on Wikipedia
Fuzzy logic tutorial
Think Bayes: Bayesian Statistics Made Simple (with Python code)