Simulating drinking in social networks to inform alcohol prevention and treatment efforts

Abstract

Adolescent drinking influences, and is influenced by, peer alcohol use. Several efficacious adolescent alcohol interventions include elements aimed at reducing susceptibility to peer influence. Modeling these interventions within dynamically changing social networks may improve our understanding of how such interventions work and for whom they work best. We used stochastic actor-based models to simulate longitudinal drinking and friendship formation within social networks using parameters obtained from a meta-analysis of real-world 10th grade adolescent social networks. Levels of social influence (i.e., friends affecting changes in one's drinking) and social selection (i.e., drinking affecting changes in one's friendships) were manipulated at several levels, which directly impacted the degree of clustering in friendships based on similarity in drinking behavior. Midway through each simulation, one randomly selected heavy-drinking actor from each network received an "intervention" that either (a) reduced their susceptibility to social influence, (b) reduced their susceptibility to social selection, (c) eliminated a friendship with a heavy drinker, or (d) initiated a friendship with a nondrinker. Only the intervention that eliminated targeted actors' susceptibility to social influence consistently reduced that actor's drinking. Moreover, this was only effective in networks with social influence and social selection that were at higher levels than what was found in the real-world reference study. Social influence and social selection are dynamic processes that can lead to complex systems that may moderate the effectiveness of network-based interventions. Interventions that reduce susceptibility to social influence may be most effective among adolescents with high susceptibility to social influence and heavier-drinking friends. (PsycINFO Database Record

Figure 1

Simulation procedures. Networks are first generated with completely random ties and drinking statuses (A), then evolve according to the network and behavioral parameters specified in text (B). Five copies of each network are then produced and the same heavy-drinking network member is targeted for a different intervention in each network copy (C). The network then evolves again according to the same parameters in Table 1 and the drinking status of the target actor is recorded at the conclusion of the simulation (D). The process is then repeated for 500 trials per condition. Shades of nodes indicate actor drinking statuses, lines between nodes indicate social ties between actors, encircled node indicates heavy-drinking actor targeted for intervention. The simulated networks each included 287 actors; only 6 actors are shown here for illustration. Darker nodes indicate heavier drinkers; lines between nodes indicate social ties.

Figure 2

Clustering by drinking status. Clustering represented as mean Pearson correlations between drinking statuses and the mean drinking statuses of friends. See Table 1 for explanation of social influence and social selection values.

Figure 3

Example network graphs and distributions of drinking statuses within networks.

Figure 4

Main effects of interventions on target actor drinking. Bar graphs indicate the mean drinking status of actors targeted for intervention after completion of the simulation. Vertical lines indicate ± 1 standard error of the mean. Treatments that significantly differed from the control are indicated by asterisks. See Table 1 for explanation of social influence and social selection values. *p<.05, **p<.01, ***p<.001, all p-values are adjusted using Bonferroni correction. Ctrl=control condition, Infl=eliminating susceptibility to social influence, Sel=eliminating susceptibility to social selection, +tie=extending new tie to non-drinking actor, −tie=removing existing tie to heaviest drinking friend.

Figure 5

Moderation of treatment effects by pre-treatment friends’ drinking. Diagonal lines indicate the associations between friends’ average drinking status before treatment (x-axis) and post-treatment target actor drinking (y-axis) for each of the treatment conditions (separate lines). The control condition is indicated by a dashed line and only treatment conditions with significantly different slopes from the control condition are labeled to facilitate readability. Black histograms at the bottom of each plot indicate the distributions of friends’ average drinking status before treatment. Infl = treatment condition that eliminated susceptibility to social influence. See Table 1 for explanation of social influence and social selection values. **p<.01, ***p<.001, all p-values are adjusted using Bonferroni correction.