NetLogo: A multi-agent programmable modeling environment
ccl.northwestern.edu
ccl.northwestern.edu
Usually you write a simple model, it does something pathological like diverging to one extreme, you maybe tune the parameters a bit until it does something that fits better with your expectations, then you publish. But the behavior you observed is still pathological, dependent on fine tuning of the model parameters, and has nothing to do with the relationship between the model and reality.
I especially panicked when I saw people on HN arguing we should drop all those SIR-based models for Covid-19 and use multiagent simulations instead.
Also, NetLogo obfuscates from you that usually what you're trying to model is a very simple one-dimensional equation that could be written in three lines of Python, and that you could see the problems with immediately if you'd look at the actual equation.
This is a good example: https://arxiv.org/pdf/1802.07068.pdf
My own review is in Hebrew, but it seems like this is a good one: http://joshuaballoch.github.io/luck-in-life-still-misunderst...
Basically, a mirror of modern DNN approaches: data driven correlations and hyperparemter tuning. Main difference are that with MAS, humans are usually tuning (this is changing though) vs DNN approaches, the tuning is algorithmically driven.
The one benefit I would claim is that with these MAS, you at least know which base assumptions you're really tweaking to converge on states of interest. The question is (as always) if you've accurately: reduced the problem and represented principle interactions in the model. Given the complexity of real world systems MAS try to model, I think this is optimistically incredibly challenging and more realistically, nearly impossible to achieve currently. I think it's still a useful modeling approach that should be explored though I feel it's still in it's infancy compared to numeric, analytic, statistical, etc. models. I think if its taken the surge of computing infrastructure to drive the current successes we're seeing in DNN, it may take an equally larger surge to see improvements in MAS.
Stuff like sensitivity analysis is both easy to do and part of standard operating procedures when working with DNNS, but not when modeling with NetLogo.
I've also been lucky enough to meet some of the people who worked on this during my career...
Truly a great educational tool!
I wish I had more time to participate! I noticed an interesting Human-in-the-Loop Learning (HILL) challenge running BattleSnake with AWS SageMaker. It's a well-known, time-tested result. Agents + Humans consistently outperform either humans or agents alone ;)
Battlesnake Challenge: A Multi-agent Reinforcement Learning Playground with Human-in-the-loop
https://www.complexityexplorer.org/courses/101-introduction-...