Value of information
- Reed with uniform large noise, logistic map
- Reed with uniform large noise
- Reed with uniform small noise
- Sethi with uniform small noise, logistic map.
- Sethi with large noise Doesn’t work. hmm..
- voi_sethi_parameters.md , running value of information setup but with small noise & logistic map. Whoops, this example solves uniform noise but simulates log-normal noise, which probably explains the positive effect of growth noise once again.
- Corrected Sethi simulation small, uniform noise and logisitic map. Confirms Mike’s hypothesis that the benefit to growh noise came from the assymetry of the log-normal noise, as the benefit is gone in this case. The policy functions all look like constant escapement rules, as expected for the “all low” noise limits.
Re-running with larger noise.
Also ran original value of information with gaussian noise.
Critical transitions and early warning
- Example with underlying critical transition externality
- added calculation of warning signals and distribution of resulting stats, and shows the state equation from May (1977).
- May (1977) example of transitions in optimal management without any deterioration of environment / approaching shifts.
Next, use specific distribution and probabilities of a transition (ROC curve). See table, eqn
- Simulate timeseries before management begins, use this data to compute warning signals.
- Calculate the ROC curve
- Calculate the expected profits remaining under the hypothesis that the system is not going to collapse. “D”
- Calculate the expected profits under the assumption that the system collapses (in the next timestep?) “C” == 0
- Calculate the profits expected under a response to the signal. (Say, 50% reduction in harvest). “A”, “B”
Ideal response with complete information: determine the optimal policy under temporally changing f.
Proposed edits to the earlywarnings package
- separate out / functionalize each signal in
generic_ews, separate plotting from evaluating.