# Notes

## Multiple-uncertainty

• Created smaller version of table to simplify comparisons
• Switched to stationary policy comparisons only. (Running out over longer time shifts total value a bit but doesn’t amplify scale of the differences much)
• Larger noise amplifies the effects (compare 0.3 levels to 0.5 levels)
• note that averaging over replicates gives rather consistent means. The differences between successive runs of the algorithm give results agreeing to the tenths place at least.

## Tables of results

Note that columns represent the decision-maker’s beliefs about uncertainty and rows represent the true uncertainty present in the simulation.

det g m all
det 19.11 19.11 18.81 18.81
g 18.92 18.63 18.74 18.56
m 16.17 16.76 17.38 18.08
all 15.54 14.65 16.50 17.15
det g m all
det 0.00 0.00 0.00 0.00
g 4.41 4.31 3.59 3.28
m 3.10 2.96 1.60 1.12
all 5.06 4.89 4.20 4.59

### logistic case

• Overcompensatory density dependence is tough on stock. With given discounting (5%) and finite horizon (15 cycles) strategies act conservatively. Hmm, strange that deterministic case is equally effected..

• running with much weaker r to reduce overcompensatory impacts…

### Coding

Attempting to translate algorithm into Jim’s native tongue (matlab) for feedback. R version vs matlab not there yet…

## Reviewing

• Another review request, another review done.

## Misc

• With Alistair, playing around with this HMM EM algorithm gui11aume/HMMt. Also threw in a pull request with documentation. grr, took a bit to understand what was going on with that paper.