Using an agent-based model to simulate the development of risk behaviors during adolescence

Adolescents tend to adopt behaviors that are similar to those of their friends, and also tend to become friends with peers that have similar interests and behaviors. This tendency towards homogeneity applies not only to conventional behaviors such as working for school and participating in sports activities, but also to risk behaviors such as drug use, oppositional behavior or unsafe sex. The current study aims at building an agent model to answer the following related questions: How do friendship groups evolve and what is the role of behavioral similarity in friendship formation? How does homogeneity among peers emerge, with regard to conventional as well as risk behaviors? On the basis of the theoretical and empirical literature on friendship selection and influences on risk behavior during adolescence we first developed a conceptual framework, which was then translated into a mathematical model of a dynamic system and implemented as an agent-based computer simulation consisting of simple behavioral rules and principles. Each agent in the model holds distinct property matrices including an individual behavioral profile with a list of risky (i.e., alcohol use, aggressiveness, soft drugs) and conventional behaviors (i.e., school attendance, sports, work). The computer model simulates the development, during one school year, of a social network (i.e. formation of friendships and cliques), the (dyadic) interactions between pupils and their behavioral profiles. During the course of simulation, the agents' behavioral profiles change on the basis of their interactions resulting in individual developmental curves of conventional and risk behaviors. These profiles are used to calculate the (behavioral) similarity and differences between the various agents. Generally, the model output is analyzed by means of visual inspection (i.e., plotting developmental curves of behavior and social networks), systematic comparison and by calculating additional measures (i.e., using specific social analysis software packages). Simulation results conclusively indicate model validity. The model simulates qualitative properties currently found in research on adolescent development, namely the role of homophily, the appearance of friendship clusters, and the increase in behavioral homogeneity among friends. The model not only converges with empirical findings, but furthermore helps to explain social psychological phenomena (e.g. the emergence of homophily among adolescents)

Suicide Attempts from Adolescence into Young Adulthood: A System Dynamics Perspective for Intervention and Prevention

Though the reduction of suicide-related deaths has been a national priority for over a decade (U.S. Department of Health and Human Services, 2001) and over $22 million per year (National Institutes of Health, 2015) have been invested to prevent suicide, rates of suicide have not declined (CDC, 2012). In fact, for some groups of adolescents, these rates seem to be on the ride (Wasserman, Cheng, & Jiang, 2005). The ineffectiveness in reducing deaths by suicide despite increased funding and coordinated efforts suggests the need for a new perspective on examining why and how adolescents begin to desire and attempt suicide and how to stop new attempts from occurring. Using an individual-level system dynamics model (Forrester, 1994; Sterman, 2000), this study answers the following research questions: 1. Is there a feedback relationship governing the experience of suicide attempts for adolescents into adulthood? 2. What types of interventions can be used to decrease suicidality across the lifespan? The goal of this study was to understand whether Thomas Joiners interpersonal theory of suicide (IPTS) (Joiner, 2005; Van Orden et al., 2010), when mathematically defined as a system dynamics model, could accurately simulate and predict suicide attempts across time. The model was specified with nationally representative data from the National Longitudinal Survey for Adolescent and Adult Health (Add Health) and tested for applicability in understanding differences in suicide attempts by gender and racial subgroups. Modifications to the structure of the model were made leading to a modified theory, the developmental systems model of the interpersonal theory of suicide. Results from experiments on the developmental systems model of IPTS suggest that reducing the duration of depression or increasing the time it takes to build capability to attempt suicide for adolescents can minimize attempts across adolescence and adulthood. Implications for research, policy, and practice are outlined, with an emphasis on future directions for suicide research

Virtual agents and risk-taking behavior in adolescence: the twofold nature of nudging

Peer pressure can influence risk-taking behavior and it is particularly felt during adolescence. With artificial intelligence (AI) increasingly present in a range of everyday human contexts, including virtual environments, it is important to examine whether AI can have an impact on human's decision making processes and behavior. By using the balloon analogue risk task (BART) evaluating propensity to take risk, in this study 113 adolescents' risk-taking behavior was measured when playing alone and in the presence of either a robot avatar or human avatar. In the avatar conditions, participants performed the BART while the avatars either (1) verbally incited risk-taking or (2) discouraged risk-taking (experimental tasks). Risk-taking behavior in the BART was assessed in terms of total number of pumps, gain and explosions. Tendency to impulsivity was also evaluated, as well as the effects of age and gender on risky behavior. The main finding showed a significant effect of both avatars on risk-taking tendency, with riskier behavior during incitement than discouragement conditions, the latter being also substantially different from the playing-alone condition. The results of this study open up new questions in a very sensitive and timely topic and offer various insights into the effect of nudging on adolescents' behavior in virtual contexts

System Dynamics Modeling for Childhood Obesity

Effective strategies for prevention of obesity, particularly in youths, have been elusive since the recognition of obesity as a major public health issue two decades ago. In general, obesity is a result of chronic, quantitative imbalance between energy intake and energy expenditure, which is influenced by a combination of genetic, environmental, psychological and social factors. Therefore, a systems perspective is needed to examine effective obesity prevention strategies. In this study, a systems dynamics model was developed using the data from the Girls health Enrichment Multi-site Studies (GEMS). GEMS tested the efficacy of a 2-year family-based intervention to reduce excessive increase in body mass index (BMI) in 8-10 year old African American girls. First, an optimum model was built by systematically adding variables to fit the observed data by regression analysis for 50 randomly selected individuals from the cohort. The final model included nutrition, physical activity, and several environmental factors. Next, the model was used to compare two intervention strategies used in the GEMS study. Consistent with previous reports, we found that the two strategies did not affect the BMI increases observed in this cohort. Interestingly however, the model predicted that a 10 min increase in exercise would decrease BMI in the group receiving behavioral counseling. Our work suggests that system dynamics modeling may be useful for testing potential intervention strategies in complex disorders such as obesit

A hybrid approach with agent-based simulation and clustering for sociograms

In the last years, some features of sociograms have proven to be strongly related to the performance of groups. However, the prediction of sociograms according to the features of individuals is still an open issue. In particular, the current approach presents a hybrid approach between agent-based simulation and clustering for simulating sociograms according to the psychological features of their members. This approach performs the clustering extracting certain types of individuals regarding their psychological characteristics, from training data. New people can then be associated with one of the types in order to run a sociogram simulation. This approach has been implemented with the tool called CLUS-SOCI (an agent-based and CLUStering tool for simulating SOCIograms). The current approach has been experienced with real data from four different secondary schools, with 38 real sociograms involving 714 students. Two thirds of these data were used for training the tool, while the remaining third was used for validating it. In the validation data, the resulting simulated sociograms were similar to the real ones in terms of cohesion, coherence of reciprocal relations and intensity, according to the binomial test with the correction of Bonferroni

"Stopping before you start" : reducing and preventing initiation of tobacco use in the ACT

Tobacco is the leading cause of preventable death in Australia and contributes to 5.4% of disease burden in the Australian Capital Territory. Initiation of tobacco use is most likely to occur during adolescence and young adulthood (at less than 20 years). Prevention of tobacco initiation involves a combination of regulatory, educational and health promotion interventions including restrictions on the sale of tobacco products. This paper reports on the development and use of an agent-based model to explore the impact of modifying three hypothetical regulatory and health promotion interventions: 1) increasing the minimum purchasing age for tobacco products, 2) reducing retail sales of tobacco products to persons under the minimum purchasing age and 3) reducing secondary sharing of tobacco products to persons under the minimum purchasing age using health promotion messaging. The model was built using a participatory approach that engaged policy officers, health promotion officers, epidemiologists, biostatisticians and computer scientists. The structure of the model included interacting state chart representations of smoking and level of concern about tobacco use (engagement status) and a pro-smoking score, which defined the hazard rate of initiation, cessation, and relapse. The pro-smoking score was a function of several risk factors including engagement, social effect of having more or fewer smoking peers, addiction and withdrawal levels and access to tobacco products. Parameterisation of the model drew on a range of data sources with local data being prioritised where it was available. A series of scenarios comparing the impact of the interventions on smoking prevalence rates and age of initiation are reported. Of the three interventions simulated, increasing the minimum purchasing age from 18 to 21 years had the greatest impact on smoking prevalence across the population, reducing the prevalence of smoking from 8.5% (95% CI 7.8, 9.2) to 6.9% (95% CI 6.4, 7.4) five years post-intervention and 4.1% (95% CI 3.8, 4.3) 20 years post intervention (Figure 1). The interventions aimed to reduce the sale of tobacco products to minors and reduce secondary sharing produced small reductions on their own. However, when implemented in combination with increasing the minimum purchasing age, they significantly increased the impact of this intervention from ten years post-implementation, ultimately resulting in a prevalence rate of 2.8% (95% CI 2.6, 3.0) 20 years post-implementation. Given the challenges associated with ceasing tobacco use, these in silico experiments demonstrate the importance of regulatory public health interventions to delay, and therefore potentially prevent initiation

Social learning across adolescence: A Bayesian neurocognitive perspective

Adolescence is a period of social re-orientation in which we are generally more prone to peer influence and the updating of our beliefs based on social information, also called social learning, than in any other stage of our life. However, how do we know when to use social information and whose information to use and how does this ability develop across adolescence? Here, we review the social learning literature from a behavioral, neural and computational viewpoint, focusing on the development of brain systems related to executive functioning, value-based decision-making and social cognition. We put forward a Bayesian reinforcement learning framework that incorporates social learning about value associated with particular behavior and uncertainty in our environment and experiences. We discuss how this framework can inform us about developmental changes in social learning, including how the assessment of uncertainty and the ability to adaptively discriminate between information from different social sources change across adolescence. By combining reward-based decision-making in the domains of both informational and normative influence, this framework explains both negative and positive social peer influence in adolescence