A system dynamics approach to the study of Colombian coca cultivation and the counter-intuitive consequence of law enforcement

Organized crime requires dynamic decision making

It is extremely hard to successfully fight organized crime, not only because there are ambiguous and complex interactions between factors and actors, but also because organized crime is volatile and adaptive to changing conditions. This paper argues that, in addition to acquiring knowledge on criminal processes and behavior, we also need a better understanding of organizational decision-making processes to select the most effective and sustainable interventions aimed at organized crime. To date, one-shot decisions from a single organizational perspective are predominant in fighting organized crime. This type of decision often ignores the system response to the intervention. There is a need to acknowledge the dynamic nature of criminal behavior and networks, and to take that into account in the design of an intervention strategy. Such an approach entails a sequence of interdependent steps, iteratively applied, to reach sustainable effects. However, this way of decision making does not come naturally for most people. This paper concludes that investments should be made in training and decision support for teams fighting organized crime.

Insights from system dynamics applications in addiction research: A scoping review

BACKGROUND AND AIMS

Substance misuse and use disorders are dynamic and complex problems, situated within systems of interacting social, environmental, and neurobiological factors. System dynamics (SD) methods broaden, test, and improve understanding of complex systems and can help inform effective action. We sought to systematically review the use of SD tools in addiction-related research.

METHODS

Following PRISMA guidelines, we searched several databases from 1958 to 2019. We included studies focused on addiction-related screening and diagnosis, treatment, and return to use, as well as studies focused on earlier stages that may begin a path to addiction (e.g., experimentation, misuse onset).

RESULTS

We extracted information from 59 articles with a median publication year of 2014. In addition to using SD to understand the underlying complexity driving addiction-related trends, other commonly cited reasons for use of SD included assessing impacts of potential actions (n = 35), predicting future trends (n = 28), and supporting strategic planning processes (n = 22). Most studies included simulation models (n = 43); however, some presented insights from qualitative SD diagrams (n = 9) and concept models (n = 6). The majority of studies focused on stages leading to potential addiction: initiation/ experimentation (n = 42) and misuse onset (n = 38). One-third (n = 20) engaged persons with lived experience or other stakeholders during the modeling process.

CONCLUSIONS

Addiction-related SD research has increased over the last few decades with applications varying in several ways, from model purpose and types of data used to stakeholder involvement. Future applications should consider the benefits of stakeholder engagement throughout the modeling process and expanding models to include concomitant substance use.