Jeremy Needle
EECS 472 Final Project Progress Update
2013-5-20

## Changes preceded by '+'

1. Agent behavior: How do the agents behave/work?
	- At each step, child agents sample adult agents’ ‘language’; then, the children fix their own language statistically.
		+ In the base form, the children use a placeholder learning procedure (they all learn the mean f1,f2 from the whole adult population; this means they instantly converge to a set result).
	- Adults die, children become adults, and new children are created.
	+ Because f1,f2 are often used to represent a 2D 'vowel space', the turtles are moved in the view to represent their position in such a space.
2. System behavior: How does the overall system behave/work? 
	- Due to randomness and selection in the ‘fixing’ process, the language as a whole evolves over time.
		+ In the base form, this is not the case: the pure mean (placeholder) learning procedure means instant convergence and stability.
3. Rationale for agent rules: Why did you give the agents these rules? 
	- Listening and learning mimics a very simple model of child language acquisition, and some traditional models of language change.
		+ Different theories exist for child (statistical) learning strategies, so multiple such methods will be implemented.
4. Model output: Do you think your model currently provides a good description of the system’s behavior? Why or why not? 
	- The model is not functional yet, but the basic version is likely too simplified to describe the process adequately.
		+ With the base form implemented, we do see that the pure mean method contains no randomness or bias, so there is no successive change.
5. Questions: What questions do you have about your model? 
	- The biggest question is which and how many of the proposed elaborations are necessary (or reasonable).
	+ Specifically, additional learning procedures need to be selected.
	+ In addition, it might be appropriate at this point to already add locality effects (global listening at minimum exacerbates the pure mean stability effect).
6. Next steps: Briefly list your next steps for improving the model. 
	- The first step is getting the base form running, then implementing random lifespan.
	+ Apart from random lifespan, alternative learning procedures are the crucial next step.