Critical TransitionsMidterm Report
Keith Heyde
Diks et al. 2012
What Are Critical Transitions?
Early Warning Signs
1) Critical Slowing
2) Asymmetry of Fluctuation
3) Flickering (with stochastic magnitude)
Predicting Critical Transitions: Case Study
Lake Eutrophication
Wang et al. 2012
Critical Slowing
Slow Perturbation Recovery
Increased autocorrelation
Increased Variance
- The focus of my analysis thus far has been identifying critical slowing in certain metrics
Previous Successful (Published) Examples
Stock Market (mixed results)
Climate – Flickering and critical slowing at Younger Dryas Cold Period
Ecosystems- Vegetation and Desertification
Agri/Aquaculture- Fishing stocks
Neurological- Epilepsy/ Depression
Leemput et al. 2013
Methods PursuedFind Sample
Data
Understand potential
chaotic drop
If smooth add noise
(matlab)
Examine autocorrelatio
n and skewness
If ‘stochastic’ leave as is
Examine autocorrelatio
n and skewness
Examples Pursued
Splitting States
Nationalization/Privatization of Industry
- Mining in Chile
- Oil Reserves in Latin America (country by country)
Venture capital Investment patterns by industry
In all cases data was taken from The Economist (in turn taken from primary sources)
Moving forward:
Predicting Antibiotic Resistance
1) Normal (mutation) Death Response
2) Altruistic Death Response
Yurtsev et al.
Moving Forward Cont..
• Parameters: public good production (B2)
• Multiple equilibria (including zero)
• Sample data processing within MATLAB (autocorrelation and variance analysis)
Tanouchi et al. 2012
Have a Great Day!
And thanks to Prof. Ross for all the help!