Table 3 Characteristics of complex systems with corresponding scientific and clinical implications
From: Complexity in psychological self-ratings: implications for research and practice
| Characteristic complex system | Scientific implications | Clinical implications |
|---|---|---|
| Memory |
• Absence of long-range temporal correlations and stationarity of temporal correlations cannot be assumed, but should be examined • The data-generating process of EMA data is likely to involve interactions across scales • Future research should explore techniques that do not make assumptions about the correlation structure of EMA data such as recurrence analysis or convergent cross mapping |
• Current psychopathology should be understood as emergent from a life-span history of interaction events • Patients’ specific psychopathological states are fundamentally individualized |
| Regime shifts |
• Stationarity of mean and variance cannot be assumed, but should be examined • Different regimes in a time series demand their own description • Future research should further study drivers and predictors of phase transitions |
• Enduring clinical improvement may be understood as a phase transition • Successful treatments are then characterized by a destabilization period in which the patient’s psychological state is more variable • Interventions are hypothesized to be more effective during periods of destabilization |
| Sensitive dependence on initial conditions |
• Long-term prediction of psychological self-ratings may be fundamentally impossible • Future research should focus on short-term prediction |
• Frequent process monitoring is essential to track the change process • Few measurements may give a misleading impression of the clinical change processes |