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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