BACKGROUND: Computer simulations provide a useful tool for bringing together diverse sources of information in order to increase understanding of the complex aetiology of drug use and related harm, and to inform the development of effective policies. In this paper, we describe SimAmph, an agent-based simulation model for exploring how individual perceptions, peer influences and subcultural settings shape the use of psychostimulants and related harm amongst young Australians. METHODS: We present the conceptual architecture underpinning SimAmph, the assumptions we made in building it, the outcomes of sensitivity analysis of key model parameters and the results obtained when we modelled a baseline scenario. RESULTS: SimAmph's core behavioural algorithm is able to produce social patterns of partying and recreational drug use that approximate those found in an Australian national population survey. We also discuss the limitations involved in running closed-system simulations and how the model could be refined to include the social, as well as health, consequences of drug use. CONCLUSION: SimAmph provides a useful tool for integrating diverse data and exploring drug policy scenarios. Its integrated approach goes some way towards overcoming the compartmentalisation that characterises existing data, and its structure, parameters and values can be modified as new data and understandings emerge. In a companion paper (Dray et al., 2011), we use the model outlined here to explore the possible consequences of two policy scenarios.
BACKGROUND: Computer simulations provide a useful tool for bringing together diverse sources of information in order to increase understanding of the complex aetiology of drug use and related harm, and to inform the development of effective policies. In this paper, we describe SimAmph, an agent-based simulation model for exploring how individual perceptions, peer influences and subcultural settings shape the use of psychostimulants and related harm amongst young Australians. METHODS: We present the conceptual architecture underpinning SimAmph, the assumptions we made in building it, the outcomes of sensitivity analysis of key model parameters and the results obtained when we modelled a baseline scenario. RESULTS: SimAmph's core behavioural algorithm is able to produce social patterns of partying and recreational drug use that approximate those found in an Australian national population survey. We also discuss the limitations involved in running closed-system simulations and how the model could be refined to include the social, as well as health, consequences of drug use. CONCLUSION: SimAmph provides a useful tool for integrating diverse data and exploring drug policy scenarios. Its integrated approach goes some way towards overcoming the compartmentalisation that characterises existing data, and its structure, parameters and values can be modified as new data and understandings emerge. In a companion paper (Dray et al., 2011), we use the model outlined here to explore the possible consequences of two policy scenarios.