Session: Employing Complexity Theory and Computational Approaches to Improve Services for Foster Youth with the Most Complicated Histories (Society for Social Work and Research 26th Annual Conference - Social Work Science for Racial, Social, and Political Justice)

Foster youth are a heterogenous group of children and youth, each with unique histories. Some have complicated histories that include various combinations of experiences that are likely to impede youth development, including trauma and instability. Among those with the most complicated histories are those who experience high placement instability and those preparing to age out of care. In this symposium, we present three studies that illustrate how complexity theory and computational approaches may be critical in helping us understand and serve these children and youth with the most extreme experiences. First, we provide a conceptual foundation by explaining complexity theory. Then, we present two empirical studies, one which employs geographic information systems (GIS) and the second which employs social network analysis (SNA). We conclude with thoughts from our discussant and expert on complex adaptive systems, Dr. Michael Wolf-Brangin.

In the first study, we explain how complexity theory provides a holistic framework for approaching child welfare phenomena. This theoretical approach moves beyond examining each part of the system separately to a focus on the whole system, taking into account the dynamic interactions between components of the system. There has been some uptake of complexity theory in social work research, but computational methods, such as geographic information systems (GIS), social network analysis (SNA), system dynamics modeling, and agent-based modeling are necessary to examine and test frameworks derived from complexity theory.

Second, we use GIS and spatial statistics to explore the geographic complexities of multiple foster care placement using 15 years of foster care placement data. We use spatial hierarchal models and distance measurements to examine the placement change from the child's home before entering foster care and the last foster care placement. This study shows how GIS can help child welfare workers understand and analyze the spatial components of foster care placements in order to minimize placement instability.

The third study uses baseline data from a longitudinal pilot study that will characterize how the social networks of foster youth aging out of care change through their involvement in a mentoring program. We use SNA to compare their baseline networks to a convenience sample of undergraduate students at a four-year university. We discuss how insights from these comparisons can inform the cultivation of social network that are most likely to provide the necessary supports for these foster youth as they are aging out of care.

There have been calls for the use of GIS (Hillier, 2007) and social network analysis (Blakeslee, 2012) in social work research. We echo and extend these calls. It is the application of complexity theory, in combination with these computational approaches (i.e., computational social science), that have the potential to supplement existing knowledge by moving beyond linear, reductionist knowledge-building approaches to explicitly taking into account the youth’s social and environmental contexts. In this way, we can advance our knowledge of the foster youth with the most complicated histories.