Summarizing Earlier Warning Signal Under Control Notes

  • NSF goals:
  • combine control methods and warning signals
  • do non-parametric or machine learning increase or decrease chance of transition
  • learning
  • Further areas (from comment piece)
  • baselines (statistical; emperical)
  • context-specific (additional data sources, reflecting context in model)

Warning signals in managed systems

Management decreases the signal: compare likelihood statistic in unmanaged_warning managed_warning:


Estimating Stability in managed systems

Recovery rate in optimally fished vs unfished system (based on the OU model):

OU parameters over replicates
OU parameters over replicates
  • How would a use of a signal feed back into a control algorithm? Need probabilities of possible models for driving a belief SDP?


Learning about a tipping point: models are the correct allee-threshold (Myers) model, and the best-fit Beverton Holt model. Inability to learn fast enough leads to crashes

  • Active learning about parameters can be worse than not learning?