Association for Behavior Analysis International

This symposium will examine the issue of global poverty and analyze policies aimed to reduce poverty from a behavioral perspective. Presenters will offer a conceptual framework for chronic poverty and considerations in collecting and interpreting data related to public policy issues including child welfare practice. Potential actions and technological advancements will be discussed for providing adequate social services and breaking the cycle of chronic poverty to achieve a better world.

Behavior analysts have long recognized the benefits of closely following their data; however, the data we are following may be changing faster than the tools we employ to analyze it. Recently, a variety of parametric and non-parametric statistical techniques have become popular alternatives to our foundational tactics in scientific research--even when the data at hand defies the underlying critical statistical assumptions. This problem even saturates behavior analytic investigations that focus on the evaluation of complex data related to public policy issues in areas such as poverty, geriatrics, and child welfare practice. Indeed, it is not uncommon to find statistical artifacts and anomalies tainting articles within many of our most prestigious journals within the disciplines of social services, criminal justice, and urban development. To further complicate this issue, many of our current technological advances are generating nonlinear, non-independent, and non-normal outcomes. In the face of this research enigma, there exists a much more powerful and precise set of classification and prediction platforms for researchers in the behavioral sciences. Unlike conventional statistical strategies, these systems do not entail critical assumptions pertaining to linearity, homoscedasticity, statistical independence, or normality. In this paper, we explore the many ways in which several artificial intelligence systems and related neural network models are capable of accurately classifying and predicting outcomes employing data that is inconsistent with the assumptions underlying conventional statistics. We argue that these models have potential application to a broad spectrum of behavior analytic goals.

Dr. Jomella Watson-Thompson is an Associate Professor in the Department of Applied Behavioral Science, and the Director of the Center for Service Learning at the University of Kansas. She is also an Associate Director with the Center for Community Health and Development. She attained a Ph.D. in Behavioral Psychology and a Masters of Urban Planning from the University of Kansas. She applies behavioral science methods and interventions to improve how communities address issues related to community health and development. Her research has focused on behavioral-community approaches to neighborhood development, substance abuse prevention, and youth and community violence prevention. Dr. Thompson supports community-engaged scholarship using participatory approaches to address social determinants or factors that may contribute to disparities, particularly for marginalized groups and communities. She has researched the effects of community-based processes and behavioral-community interventions to promote mobilization and change in communities.  Dr. Thompson has co-authored articles on community capacity-building, youth development, and prevention and received numerous federal, state and local funding awards.  She is as an Associate Editor with Behavior and Social Issues.  Dr. Thompson has extensive experience providing training, technical support and evaluation for coalitions and community-based initiatives.

Although the world has made huge efforts to overcome global poverty, 10% of the world's population still lives in extreme poverty without access to the most basic needs such as water, sanitation, health, and education. Some families experience this condition for many years facing a situation that is difficult to emerge from. Previous studies have shown that the constraints of poverty induce a focus on immediate and safe payoffs. This situation becomes a cycle that perpetuates from generation to generation. This presentation will explore ways policy can be designed to incentivize individuals in the lower socioeconomic class to change their decision-making behaviors. Based on a behavioral perspective, we will: 1) present a conceptual framework for chronic poverty, 2) discuss government policies aimed at reducing poverty, and 3) illustrate how intersectoral actions can provide adequate social services to alter the options faced by impoverished families and help address poverty and social exclusion.