
NetLogo: A multi-agent programmable modeling environment - Rexxar
https://ccl.northwestern.edu/netlogo/
======
SonOfLilit
Whenever I see a NetLogo based paper, my suspicion is immediately raised. It's
not that multiagent simulations are never good models of reality, it's that
they are so hard to get right and so easy to shoot yourself in the foot with
and not notice. Nonlinear dynamics are _hard_.

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](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...](http://joshuaballoch.github.io/luck-in-life-still-
misunderstood/)

~~~
Frost1x
>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.

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.

~~~
SonOfLilit
Not at all similar in my opinion. Hyperparameters don't usually directly
affect the model the DNN implements, they mostly affect the speed of
convergence.

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.

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carlob
I was exposed to this as a teenager some 20 years ago (when it was called
StarLogo) and it was one of the main reasons why I got interested in
collective behavior and emergence.

I've also been lucky enough to meet some of the people who worked on this
during my career...

Truly a great educational tool!

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ArtWomb
The power of community multi-agent simulations lie in participatory
environments. Every geospatially dispersed team shares their collected data
and eventually this is how the world gets solved.

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://arxiv.org/abs/2007.10504](https://arxiv.org/abs/2007.10504)

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bmitc
The Santa Fe Institute's Complexity Explorer offers a course called
_Introduction to Agent-Based Modeling_ that uses NetLogo. Here's a link to an
archive of the most recent course.

[https://www.complexityexplorer.org/courses/101-introduction-...](https://www.complexityexplorer.org/courses/101-introduction-
to-agent-based-modeling-spring-2020)

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sevensor
I used NetLogo at one point in my education. It was a lot of fun. Maybe too
much fun. So easy to get sucked in to tweaking a parameter here and there and
watching the same initial conditions evolve in different directions.
Simulation researchers must have to be constantly on guard against having too
much fun in their jobs.

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blamestross
I did a lot of DEVs simulation of P2P networks in grad school with netlogo. It
is a great RAD system for agent based simulations.

