My research

My research focuses on modeling psychological phenomena, such as depression and intelligence, as complex dynamical systems. I study how psychological variables interact and evolve over time using network models and other formal modeling approaches. My work aims to advance the development, formalization, and empirical testing of psychological theories, as well as the methodologies used to build and evaluate formal models.

IAS Fellowship 

The network approach of psychology conceptualizes psychological constructs, such as psychopathology, intelligence, and attitudes, as systems of mutually interacting components, such as symptoms, cognitive abilities, and beliefs. To date, the implications of network theory have primarily been studied using the Ising model, in which the components can be in one of two states (e.g., a symptom can be either absent or present, or an attitude can be either pro or contra). While these models have yielded important insights, they may be too restrictive for systems that naturally involve three states. Examples include abstinence, moderate use, or addiction in relation to drug use; manic, euthymic, and depressed states in bipolar disorder; and pro, contra, and neutrality in attitudes.

This has important consequences, since our models influence how we conceptualize and study psychological phenomena. To address these issues, the Blume–Capel network model has recently been introduced as a psychologically meaningful, three-state extension of the Ising model. Currently, the Blume–Capel model can be estimated using cross-sectional data. However, network theory is inherently an intra-individual theory, and ultimately, we want to understand and model how people behave over time. For example, how does someone with bipolar disorder move through the stages of mania, euthymia, and depression? During my time as an IAS fellow, I intend to develop estimation techniques for the Blume–Capel model based on time series data.