摘要：Objectives . We developed a preliminary agent-based simulation model designed to examine agent–environment interactions that support the development and maintenance of drinking behavior at the population level. Methods . The model was defined on a 1-dimensional lattice along which agents might move left or right in single steps at each iteration. Agents could exchange information about their drinking with each other. In the second generation of the model, a “bar” was added to the lattice to attract drinkers. Results . The model showed that changes in drinking status propagated through the agent population as a function of probabilities of conversion, rates of contact, and contact time. There was a critical speed of population mixing beyond which the conversion rate of susceptible nondrinkers was saturated, and the bar both enhanced and buffered the rate of propagation, changing the model dynamics. Conclusions . The models demonstrate that the basic dynamics underlying social influences on drinking behavior are shaped by contacts between drinkers and focused by characteristics of drinking environments. Agent-based modeling and other computer-based simulations have been used increasingly in the social sciences since the 1990s as a means of understanding social processes and dynamics. 1 Agent-based modeling involves “growing” computerized social systems and structures based on the interactions of individual entities (“agents”). 2 These agents use simple behavioral rules local to the agent’s environment to move about their simulated environment and to interact with one another. 2 These modeling efforts enable researchers to test and develop theories in a way that might not be possible using analytic and experimental methods. For example, emotions or beliefs of the inhabitants of a simulated world such as “fear,” “grievances,” and “ethnocentrism” can be manipulated in a simulation in a way that would not be permissible for ethical reasons in an experiment. Thus, in agent-based modeling, the researcher builds an artificial environment that represents a simplified version of the real-world processes of interest and then observes the consequences of manipulating key input variables on attitudinal and behavioral outputs. 1 Agent-based modeling has proved especially useful in understanding complex social dynamics, notably those involving interactions between micro- and macrolevel processes and the development of emergent behaviors, such as racial segregation, innovations in human organizations, civil unrest, ethnic conflict, population movement, and the diffusion of innovations and fads. 3 – 7 In many of these applications, the central issue being explored is the way in which agents respond to their social context, specifically to how others around them are acting, and to the efforts of organized entities to influence them through either punitive or persuasive mechanisms of social control. 4 Although the theories addressed by agent-based models generally involve complex macrolevel social processes, the models themselves are grounded in the actions and interactions of individual agents. 2 , 3 , 8 Indeed, it is a guiding principle of agent-based modeling that many social behaviors emerge from these local dynamic interactions rather than by being shaped from above by sociostructural forces. 2 , 3 , 6 In addition, given the underlying assumption of these models—that agents employ very simple, local behavioral rules—the goal of agent-based models is to “explore the simplest set of behavioral assumptions required to generate a macropattern of explanatory interest.” 6 (p146) Investigators in the field of alcohol research are also concerned with the effects of interpersonal and person–environment interactions, which are difficult to study using analytic and experimental techniques because of practical and ethical constraints. For example, peer group affiliation and neighborhood alcohol outlet density cannot be experimentally manipulated by researchers interested in these risk factors, nor can some individuals within a community be randomly subjected to a new policy or ordinance while others are not. In addition, 2 aspects of alcohol studies make the application of agent-based models potentially fruitful. 9 First, alcohol researchers are concerned with “what if” questions: for example, “What if another bar is situated in this neighborhood?” or “What if more alcohol is sold?” 10 Second, many alcohol researchers are interested in the underlying spatial dynamics of drinking behaviors. The questions of interest here pertain primarily to person–environment interactions: for example, “What happens when people drink in 1 location and then move about their environment, coming into contact with both other drinkers and nondrinkers?” 11 Although quasi-experimental community trials and ecologic studies have proved useful in answering these questions, a widening of research approaches could substantially benefit both theory development and prevention practices. 12 We describe a preliminary agent-based model designed to explore both the social dynamics and the environmental influences that affect drinking behavior. Specifically, our model examines the interactions of 3 types of agents defined according to their current drinking status as well as by what happens to these interactions when an alcohol outlet is introduced into the environment. In line with the current literature on the dynamics of drinking behavior, our 3 types of agents are susceptible nondrinkers (those who have not started drinking alcohol and display some probability of initiating this behavior), current drinkers, and former drinkers; these are considered to be groups that do not have fixed, long-term memberships. 13 Indeed, research shows that “maturing out” and “natural recovery” are probably the rule, not the exception, among heavy and problem drinkers, 14 – 16 and that even among those classified as “alcohol dependent” as many as 75% will migrate from this class over a 1-year period. 17 And, similar to the initiation of drinking behavior, this movement into and out of heavy drinking is strongly affected by interpersonal influences and social context. 16 , 18 , 19 It is this dynamic quality and fluidity, along with the effects of social and environmental influences, that we attempted to capture in our models. In terms of the former, we hypothesized that the dynamics of interactions between classes of drinkers and the movement into and out of drinking states can be modeled mathematically. As noted above, a simple set of behavioral rules is sufficient to develop an agent-based model, and so no attempt was made to specify the exact nature of the social influence. However, the literature pertaining to social influence suggests that the processes at work range from modeling and instrumental learning to more complex group-level mechanisms that operate through social norms and context. 19 – 21 In terms of environmental influences, we hypothesized that the introduction of an alcohol outlet into the model would increase contact rates among drinkers and maintain greater numbers of drinkers in the population.
