Toward an integrative approach in the analysis of dependency problems

Area of Science:

• Addiction research
• Computational psychology
• Neuroscience

Background:

• Dependency problems encompass diverse issues such as long-term tranquillizer use, alcohol dependence, and problematic narcotic use.
• Analyzing these complex dynamic developmental processes requires a robust theoretical, epistemological, and methodological framework.

Purpose of the Study:

• To propose preliminary steps towards an integrated analysis of various dependency problems.
• To outline essential theoretical, epistemological, and methodological prerequisites for studying the dynamics of dependence.

Main Methods:

• Utilizing an artificial science neural network approach.
• Employing a mixed-method strategy incorporating diverse epistemological positions.
• Integrating empirical outputs to inform theoretical analysis.

Main Results:

• The study lays groundwork for a novel theoretical analysis of dependency dynamics.
• The proposed methodology aims to generate new insights into the complex processes leading to dependence.
• This approach facilitates the identification of refined input variables for neural network models.

Conclusions:

• An integrated analysis of dependency problems is crucial for a comprehensive understanding.
• Combining artificial neural networks with mixed methods offers a powerful strategy for studying dynamic processes.
• This research provides a foundation for advancing knowledge in addiction and dependency.