## 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.