My personal notes and projects from the Introduction to Agent-Based Modeling class offered online through the Santa Fe Institute.
A built-in tool within NetLogo to allow repeated runs over a range of parameter inputs. It can be run headless to improve speed. The instructor shows how to analyze the data output from BehaviorSpace in R. There's not a lot of context or lead up to this R section (4.5 video).
Fascinating because Prof. Schelling says he built the tipping model primarily as a teaching demonstration for his students. "If I had had computers and known how to use them and had tried to do this on a computer. I would never have understood what I was doing. I had to see it happen right on the board in front of me. Where each move responded to other moves." ~ 5:10 "[A friend helped me model it on a computer and then left me with it] and I was on my own. I found that using computer was terrific if I wanted to run a dozen or a hundred examples. But there was no way I could have known using the computer what was happening." "Bill Rand: So the physical movement on the board helped you?" "I could look at this whole checkerboard and see how when he moved someplace, that induced someone else to move some other place. I could see it all before my eyes. If I had understood how to do it by computer I would have never learned what was going on." "I'm quite convinced that if I had suggested it to this friend what I wanted to do and he'd done it for me on the computer I would have never understood what was going on."
"After I think of an idea I like to translate it into game theory, but I've never gotten an idea from game theory." "Bill Rand: Where do you get your ideas from?" "Observing."
Prof. Schelling has a great story at the end of the second interview about talking with Stanley Kubrick and the author of Red Alert as they were working on what became Dr. Strangelove.
I want to build a model to answer questions related to (engineering) team management and movement between teams as the workloads change. One potential way to do this would be to have an A and B team with different strategies for team management based on when people are requested to move to the team based on the currently available work.
- Based off of Sugarscape.
- Use the same idea of two high points in the environment for the work stream of each team
- I'd like to add more variety to the environment, such that the work streams can fluctuate over time.
- One simple way to add variety is to create the environment with random distribution of work
- In the case the landscape is random then I think agents need some cohesion through links to move as a unit
- I'd like to either use breeds or color-coding to indicate the different teams that each agent is a member of.
- I think they need links to force some level of cohesive movement. Basically to visually constrain their search area for work.
- One of the central ideas I started on this subject with is the question about time to ramp up on domain knowledge. So I think new agents who join a team need to have some time delay in getting to the maximum work rate.
- On the reverse side I think agents probably need to either be "fired" or in some way penalized for having an empty work queue for too long.
- What's the limiting factor in the model from hiring tons of workers? There has to be some counter balance to infinite hiring.
- Maybe whenever a new member joins the team it costs all the members of that breed a small amount of maximum work potential - they work a little slower for a period of time
- Fire based on FIFO?
- Fire whenever work queue is empty for X number of turns?
- Create 2 teams
- Make the environment sparser. Agents don't have to actually search, work is everywhere.
- Measure team output in a graph
- Allow different strategies for each team
Model Scope Agent Selection Agent Properties Agent Actions Environment Order of Events Inputs and Output Model Execution User Interface Model Documentation