Name
Abstract | ||
|---|---|---|
When the dynamics of an epidemic are chaotic, detailed prediction is effectively impossible, except perhaps in the short term. However, a probability distribution underlying the motion does allow for the long term prediction of statistical measures such as the mean or the standard deviation. Even this weaker long term predictability might be lost if distinct populations with chaotic dynamics are coupled. We show that such coupling can result in a phenomenon we call “sensitive dependence on neglected dynamics”. In light of this phenomenon, it is somewhat surprising that when two logistic maps are coupled, the long term predictability of the mean and standard deviation is maintained. This is true even though the probability distribution describing the time series depends on the coupling strength. The coupling-strength dependence does reveal itself in the loss of predictability of higher order moments such as skewness and kurtosis. |
Year | DOI | Venue |
|---|---|---|
2003 | 10.1023/B:OPSY.0000009553.55368.7e | Open Syst. Inform. Dynam. |
Keywords | Field | DocType |
higher order,standard deviation,time series,probability distribution,logistic map | Econometrics,Higher order moments,Predictability,Long-term prediction,Probability distribution,Coupling strength,Chaotic,Standard deviation,Kurtosis,Mathematics | Journal |
Volume | Issue | ISSN |
10 | 4 | 1573-1324 |
Citations | PageRank | References |
0 | 0.34 | 0 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Matt Davison | 1 | 46 | 8.53 |
| Christopher Essex | 2 | 36 | 8.98 |
| J. S. Shiner | 3 | 2 | 1.38 |