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human
Pls help me clean up the mess that the irresponsible gamblers I will hire as participants are going to make (mixed effects logistic regression?)

Hey everyone,

I'm trying to estimate the effect of personality on the way an individual deals with the exploration exploitation dilemma (the trade-off between the need to obtain new knowledge and the need to use that knowledge to improve performance). Participants will play a multi armed bandit game where they are instructed to collect as many points as possible. Every arm of the bandit gives a different amount of points and the total amount of attempts is limited. Since every participant will have around 20-40 obs. points, I'll need a mixed effects model, obviously. Since I'm essentially looking at a binary DV (will participants exploit the options already known or explore new ones), it should probably be a logistic regression model as well. I'm also expecting a lot of autocorrelation and a time trend (people doing less exploration and using the options already proven to be profitable more as time advances). I'm not very well versed in time series analysis, so any advice is highly appreciated!
gpt
>Since every participant will have around 20-40 obs. points, I'll need a mixed effects model, obviously. 

Obviously? How so? 

The armed bandit is a classic RL problem. What you need, is to identify the appropriate choice strategy (e.g. epsilon-greedy,  softmax, UCB, etc) that best fits the behavior of your participants. Once you have that, it will just be relating the 'temperature' (i.e. the exploration parameter) to the individual difference metric you are trying to look at.

You might want to consider starting with Sutton and Barto (1998) if this seems to be unfamiliar territory.