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!