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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