# Progress this week, by project

### Critical transitions

Must determine the correct set of comparisons:

• Solve the SDP optim using the exact time dependent
• Simulate under an f with the same escapement policy but different functional form, i.e. logistic with K & r estimated from the May example, either analytically or directly from the data.
• Considering the costs of “cautious policy” (calculates optimal harvest recursively but always selects a fraction P of the optimal.) Results in non-constant escapement rules:

Also has nonlinear (convex) impact on value.

Filing out the boxes:

• the value of no action, no shift – NPV under assumed model
• the value of no action, shift – depends when shift occurs. Assume worst?
• value of action, no shift – Action is p = ?
• value of action, shift – action is p = ? Under what shift process?
• Consider the single-timestep decision first. Then the repeated decision.

### Value of information

• logistic, lognormal (any measurement error is most conservative, then determinstic. gi the least)
• Beverton-Holt, lognormal (early version of figures) deterministic is least conservative, measurement errors, combined errors most. (Rerunning as bh_lognormal.Rmd)
• logistic, uniform (deterministic is always most conservative. gi is least)
• Beverton-Holt, uniform (deterministic is most conservative, growth noise is least!)

• constructed bias table: optimal under low/med/high r by implement under low/med/high r. (results)

### rfishbase revisions

• add fair use text, future development with fishbase.org. DONE
• Reply letter text, send to P and D. DONE
• switch to robust regression. DONE
• fix/tweak figure appearance. DONE
• Resubmit.

### Treebase

• Finish updates based on Duncan’s suggestions. DONE
• Submit 0.0-6 to CRAN. DONE
• Submit manuscript. DONE

### Working on / To Do

• rfishbase – revisions. DONE
• evolution talk
• ievobio talk
• csgf talk
• esa talk
• wrightscape
• Alan decision theory and early warning
• Jim’s precautionary paper JEEM
• PDG policy costs
• Jake uncertainty and learning