Understanding Psychological Phenomena as Complex Systems


Symposium at the Conference on Complex Systems 2018

  • Time: TBA
  • Location: TBA
  • Organizer: Jonas Haslbeck


14.30 – 15.00 Adela Isvoranu – Towards a dynamical systems approach to mental disorders
15.00 – 15.30 Giulio Costantini – Psychological dynamics in different classes of individuals 15.30 – 16.00 Oisin Ryan – Detecting Qualitative differences in Multivariate Psychological Dynamics
16.00 – 16.30 Break
16.30 – 17.00 Jonas Haslbeck – First steps towards a dynamic model of major depression
17.00 – 17.30 Gaby Lunansky – Bouncing back from adversity: Towards a dynamical conceptualization of psychological resilience
17.30 – 18.00 Jonas Dalege – Psych(w/o)metrics: A Quantum Mechanics Analogy of Psychological Measurement
18.00 – 18.30 Panel Discussion


Psychological phenomena are recognized as complex in that they are determined by a potentially huge number of variables that are interacting with each other non-linearly. Whereas traditionally psychological research has focused on verbal theories and statistical modeling of data, in recent years psychological phenomena have been increasingly approached from a complex systems perspective. This change is driven by new theoretical considerations: for instance, in clinical psychology, mental disorders have been proposed to emerge from interactions between symptoms rather than being caused by an unobserved “disease variable”. A second reason for the surge of interest in complex systems is the increasing availability of time-series measurements of individual persons using sensors and experience sampling methods. These data provide the basis for validating dynamic models empirically that has been missing so far.

In this symposium we use ideas from complex systems research to approach psychological phenomena from two sides: the first three presentations use the conceptual ideas behind complex/dynamical systems to motivate new statistical tools to analyze psychological data. Adela Isvoranu will give an introduction to network models in the field of psychology, Giulio Costantini will introduce a new type of network model that allows to abstract the complex interactions between psychological variables for different classes of individuals, and Oisín Ryan will show how to apply tools for the analysis of qualitative behaviors of dynamical systems to experience sampling time series data.

After the break, we will use complex systems ideas to approach psychological phenomena from a purely theoretical side: Jonas Haslbeck will introduce first steps towards a formalized theoretical model for major depression, Gaby Lunansky will discuss the concept of psychological resilience from a dynamical systems perspective and Jonas Dalege will introduce quantum physics as an analogy for psychological measurements to question traditional views on the psychological measurement process. The last 30 minutes are reserved for a panel discussion in which we hope to discuss how to inform theory with available empirical data and future directions in studying psychological phenomena from a complex systems perspective.


Adela Isvoranu

  • University of Amsterdam, Department of Psychological Methods
  • Title: Towards a dynamical systems approach to mental disorders

Recent years have seen a rise in the modeling of mental disorders as networks of interacting symptoms. The centerpiece of network modeling lies in the idea that mental disorders emerge from complex interactions between psychological, biological and sociological components, and that symptoms are active causal agents in producing disorder states. The patterns of interaction can be visualized in a network structure, in which variables (e.g., symptoms, environmental factors, genetic factors) are represented as nodes and the presence of an edge between any two nodes implies the existence of a statistical association. This presentation aims to provide a general introduction to the network approach to mental disorders and provide several examples of network models as constructed – from data – in the field of psycho(patho)logy.

Giulio Costantini

  • University of Milan-Bicocca, Department of Psychology
  • Title: Psychological dynamics in different classes of individuals

Several complex psychological phenomena, including for instance psychopathology and healthy personality emerge from interactions among lower-level phenomena such as cognitions, behaviors, emotions, motivations, and symptoms. Psychologists are often interested to examine whether and how the dynamics underlying different classes of individuals, such as patients and controls, are similar or different. The Gaussian Graphical Model (GGM) has been used as a parsimonious representation of between-person and within-person dynamics in psychology. GGMs are typically estimated from psychological data using the graphical lasso algorithm, which does not accommodate for multiple classes of individuals. The Fused Graphical Lasso can be used to jointly estimate GGMs in different classes and to identify similarities and differences in their underlying dynamics. We present applications of FGL to several psychological domains, including personality, situation perception, social interactions, and psychological disorders such as the post-traumatic stress disorder, pathological narcissism, and the borderline personality disorder. We discuss the most important insights afforded by this methodology for the dynamics underlying these phenomena.

Oisín Ryan

  • Utrecht University, Department of Methodology & Statistics
  • Title: Detecting Qualitative differences in Multivariate Psychological Dynamics

The increased availability of intensive longitudinal data (due to, e.g. smartphone technology) now allows researchers to study psychological phenomena, such as depression, as dynamic systems (or networks) of symptoms. Initial research in this area has focused largely on the estimation of linear models, and the examination of a) univariate aspects of such systems, or b) multivariate aspects via centrality measures. These mainstream approaches in psychology underutilize dynamic systems theory. We propose instead to examine the classification of fixed points in dynamic psychological systems to understand qualitative differences in multivariate dynamics. Specifically we will examine 1) whether (smooth) changes in individual-specific dynamic parameters over time also result in changes in the type of fixed point in the system, 2) whether differences in fixed point classification can be related to stable between-person differences, and 3) the potential of fixed point classifications to inform the targets of intervention.

Jonas Haslbeck

  • University of Amsterdam, Department of Psychological Methods
  • Title: First steps towards a dynamic model of major depression

06208f0The goal of psychopathological research is to find interventions that move patients in a state without symptoms. Recently network models of symptom data have been proposed to generate possible interventions and judge their effectiveness. While these models are attractive because they can be estimated directly from symptom data, they have two downsides: first, symptoms may not be the ideal level of analysis because they are summaries of more fundamental variables. Second, the expressiveness of these network models does not match the complexity of psychological disorders. Here we present first steps towards a realistic computational model of major depression based on coupled differential equations. We begin by simulating a functioning individual and then discuss how to formalize different etiologies of depressive symptomatology. In addition, we discuss how to support the model with data and how to maximize its usability for other researchers.

Gaby Lunansky

  • University of Amsterdam, Department of Psychological Methods
  • Title: Bouncing back from adversity: Towards a dynamical conceptualization of psychological resilience

The question of why some people develop psychopathology following adversity while others do not, has been extensively investigated in the psychological literature. A pressing yet unanswered question is what makes the latter group of people resilient in that they are seemingly able to withstand the ‘attack’ that adverse events launch on their health and well-being. Research seems to be predominantly about rather static indicators (e.g., childhood maltreatment) while the concept of resilience is deeply dynamical. Moreover, while other theoretical definitions sound dynamic, quite a few operationalizations are static in nature. To date, there is no consensus on what resilience exactly is conceptually, which seriously hampers progress as various definitions and operationalizations abound. A relatively new modeling approach conceptualizes mental disorders as a dynamical system in which the dynamics are driven by a network that consists of symptoms of a disorder that stand in certain direct relations to one another. Based on such a dynamical systems perspective, this presentation will discuss an alternative framework of psychological resilience. It will be shown how the architecture of psychopathological networks can be related to resilience indicators (e.g., connectivity of the network is related to robustness). This opens the door to a new conceptualization of resilience in the psychological field, by applying ideas from dynamical systems theory onto the concept of resilience, studying the dynamics of the new individual network models.

Jonas Dalege

  • University of Amsterdam, Department of Psychological Methods
  • Title: Psych(w/o)metrics: A Quantum Mechanics Analogy of Psychological Measurement

The main principle of our recently proposed Attitudinal Entropy framework holds that the main function of human thought is to reduce the entropy of cognitive representations. This principle has fundamental implications for psychological measurement because it creates a situation that is similar to the measurement problem in quantum mechanics – measurement is not possible without disturbing the system. Similar to the collapse of the wave function in quantum mechanics, psychological measurement causes individuals to pay attention and think about the measurement, implying that the measured construct is in fact created by the measurement (similar to the consequence of measuring a particle creates a definite state of the particle). Consequences of this implication include that (a) the usual definition of measurement error in psychometrics becomes meaningless because psychological constructs are not entities that exist independently of measurement, (b) the question of whether psychological constructs are real or not becomes an ill-posed question, (c) only measurements that have real-world analogues provide meaningful information of psychological processes, and (d) psychological measurement is inherently dynamic and not static.