
For me at least the problem was once I got their original model up and running I wasn't able to sustain the focus to construct a graphical representation that was compelling. So I never went back and debugged the stuff that I added to model (not seriously anyway).
It's sorta unfortunate that for the most part the graphics wind up being so rudimentary. Besides not really being able to convey all the things going on in the simulation the end result ends up being a lot less compelling than reading the book about it... or at least to my mind it did.
this is wrong, it's almost impossible to build a fully-rational (in the Game-Theoretic sense) agent for almost any real life usecase, except some textbook toy problems.
There are many levels of Intelligence/Cognitions for Agents.
Here's an incomplete hierarchy out of my head (the real classification will deserve a whole blog post or a paper):
- Dumb/NPC/Zero-Intelligence
- Random/Probabilistic
- Rule-based / Reflexive
- Low-Rationality
- Boundedly-Rational
- Behavioral (i.e. replicating a recorded behavior of a real-life entity/phenomena)
- Learning (e.g. using AI/ML or simple statistics)
- Adaptive (similar to learning agents, but may take different (better) actions in the same situation)
- [Fully-]Rational / Game-Theoretic
"A rational actor - a perfectly informed individual with infinite computing capacity who maximizes a fixed (non-evolving) exogenous utility function"[1] bears little relation to a human being.[2]--
[1] Aaron, 1994
[2] Growing Artificial Societies -- Joshua M. Epstein & Robert L. Axtell