159 General Discussion 7 Table 1 Comparison of component-dominant theorem versus complex system theory. Component-dominant theory behind the theory Complex systems theory General assumptions Components are static essences of a person (chapters 2 and 3) Components are best understood as dynamic processes (chapters 5 and 6) Focus on between-person differences. Within-person processes are either ignored or considered noise or measurement error (chapter 3) Focus on changes in within-person processes or between-person differences of withinperson processes (chapters 4, 5, 6) Specific (combinations of) components have specific behavioral outcomes (chapters 2 and 3) Behaviors emerge from self-organized interactions between countless components and environment (chapter 6) Directional mechanisms between components (chapters 2 and 3) Bi-directional feedback loops between components and environment (chapter 5) Structural assumptions There exists a general set of components, specific to each psychological phenomenon (chapters 2 and 3) Psychological phenomena are highly individualized: if a structure exists it is person-specific and subject to change (chapters 4 and 5) The unique contribution of components is additive (chapters 2 and 3) The whole is more than the sum of its parts It is possible to isolate the contributions of specific components from other components or the environment Interactions between countless components and the environment are inseparable, but collective variables can summarize underlying interactions (chapter 6) Linear interactions: proportional effect of input on the outcome (chapters 2, 3, and 5) Nonlinearity: disproportionality between input and outcome. Small perturbations can have large effects and at other times large perturbations may not drastically change the pattern (chapter 6) Temporal assumptions Change patterns are linear (chapters 2 and 3) Change patterns are non-linear: sudden changes between attractor states (chapters 5 and 6) Temporality can be explained on a single timescale: past value predicts behavior Interactions among countless interdependent slow and fast processes lead to the emergence of behavior (chapter 5) Processes are assumed to be stable, i.e., stationary Non-stationary is the rule rather than the exception (chapter 5) Prediction is possible on many timescales Prediction is only possible in the near future (chapter 6) Note. This table is in part based on Sosnowaka et al. (2019) and Olthof et al. (2023).
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