Doping in sport remains a pertinent threat to the integrity of sport, and to athlete health. Traditional anti-doping approaches that include deterrence, detection, and enforcement often fail to appreciate the complexity of athlete motivations, pressures, systemic incentives, cultural aspects, and the feedback loops that influence doping. While current approaches have had some success in doping prevention, doping remains persistent. A further limitation with current approaches to doping prevention is that decisions and actions aimed at prevention are often reactive and based on retrospective data and analyses in the form of test results, sanction data and statistics, investigation reports, among others. To address these gaps, the aim of this project is to develop a computational model decision support system for anti-doping stakeholders to proactively simulate preventative interventions and subsequently assess their impacts.
The project includes the following specific research objectives:
- Mapping the System – Using subject matter expert workshops, literature, and case analysis, we will construct a detailed Causal Loop Diagram (CLD) of the factors from the across the broader sports system that interact to create doping violations and vulnerabilities in Australian cycling.
- Building a Computational Model – A computational model (Systems Dynamics) will be developed based on the CLD in Phase 1. The SD model will be a digital representation the anti-doping system in cycling in Australia.
- Develop and Test Interventions – Co-design with systems stakeholders will collaboratively generate interventions for doping prevention. These interventions will then be simulated in the SD model and their impact on the overall system evaluated.
A computational decision support system will provide stakeholders with a data-driven tool to make informed choices of where to intervene for doping prevention. Such a system can simulate the effects of potential interventions before they are implemented, reducing uncertainty and the risk of unintended consequences. The outcomes will include practical, digitally tested prevention interventions ready for real-world implementation.
