Abstract: Overall, adolescents (i.e., youth ages 12–18) in treatment for substance abuse have better outcomes than those not in treatment, and multiple types of behavioral interventions hold promise (Tanner-Smith et al., 2013). Treatments with empirical support from well-designed, randomized clinical trials (RCTs) include individual-, group-, and family-based approaches. However, even with the most potent interventions tested to date, reductions in substance use observed have been modest, and robust effects on abstinence rates have been difficult to demonstrate. One candidate for enhancing outcomes is Contingency Management (CM). CM interventions are derived from an operant framework of substance abuse, which posits that substance use is initiated and maintained, in part, by the pharmacological actions of the substance in conjunction with reinforcement derived from a substance-using lifestyle (Higgins et al., 2004). Typically, CM interventions are used to engender therapeutic change within a comprehensive treatment program in a substance abuse treatment clinic. CM programs attempt to modify the substance user’s environment such that a) drug abstinence is carefully monitored, and b) reinforcing events (e.g., tangible rewards or incentives) occur when abstinence is achieved, and c) punishing events (e.g., suspension of employment or school, loss of privileges) occur when abstinence is not achieved.
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Bongard's work focuses on understanding the general nature of cognition, regardless of whether it is found in humans, animals or robots. This unique approach focuses on the role that morphology and evolution plays in cognition. Addressing these questions has taken him into the fields of biology, psychology, engineering and computer science.
Danforth is an applied mathematician interested in modeling a variety of physical, biological, and social phenomenon. He has applied principles of chaos theory to improve weather forecasts as a member of the Mathematics and Climate Research Network, and developed a real-time remote sensor of global happiness using messages from Twitter: the Hedonometer. Danforth co-runs the Computational Story Lab with Peter Dodds, and helps run UVM's reading group on complexity.
Laurent studies the interaction of structure and dynamics. His research involves network theory, statistical physics and nonlinear dynamics along with their applications in epidemiology, ecology, biology, and sociology. Recent projects include comparing complex networks of different nature, the coevolution of human behavior and infectious diseases, understanding the role of forest shape in determining stability of tropical forests, as well as the impact of echo chambers in political discussions.
Hines' work broadly focuses on finding ways to make electric energy more reliable, more affordable, with less environmental impact. Particular topics of interest include understanding the mechanisms by which small problems in the power grid become large blackouts, identifying and mitigating the stresses caused by large amounts of electric vehicle charging, and quantifying the impact of high penetrations of wind/solar on electricity systems.
Bagrow's interests include: Complex Networks (community detection, social modeling and human dynamics, statistical phenomena, graph similarity and isomorphism), Statistical Physics (non-equilibrium methods, phase transitions, percolation, interacting particle systems, spin glasses), and Optimization(glassy techniques such as simulated/quantum annealing, (non-gradient) minimization of noisy objective functions).