Machine Learning and System Dynamics: a Threat or an Opportunity?

The recent webinar presented by Hesham Mahmoud, an experienced professional in the fields of multinational corporations, academia, and the United Nations, focused on the intersection of Machine Learning (AI) and System Dynamics. This post summarizes the webinar’s key points, including the Q&A session, offering insights into how these two methodologies can be effectively integrated.

Presenter Background:

Hesham Mahmoud, currently a Junior Lecturer and Machine Learning Researcher at Radboud University, brings a wealth of experience from his roles in various sectors, including his tenure at the Food and Agriculture Organization of the United Nations. His expertise in Economics and Political Economics, combined with his professional experience, provides a unique perspective on the application of Machine Learning in System Dynamics.

Webinar Summary:

1. Defining the Relationship between AI, Machine Learning, and System Dynamics: Mahmoud began by delineating the distinctions and connections between AI, Machine Learning, and System Dynamics. He emphasized the potential of Machine Learning to enhance the analytical capabilities in System Dynamics, moving beyond the traditional view of AI as a complex and opaque field.

2. Addressing Bias and Ensuring Objectivity: The webinar highlighted the challenge of bias in Machine Learning algorithms. Mahmoud discussed the importance of feature selection and the role of human oversight in ensuring that machine-learning models are as objective and unbiased as possible.

3. Data Challenges in Machine Learning: Mahmoud pointed out that both the scarcity and abundance of data present challenges in Machine Learning. He stressed the importance of careful decision-making in feature selection to avoid introducing biases into the models.

4. Complementarity of Machine Learning and System Dynamics: The discussion underscored how Machine Learning could uncover patterns in data that might not be immediately apparent in System Dynamics models, while System Dynamics can provide a human-centric approach to guide Machine Learning analyses.

5. Practical Applications in Healthcare: The webinar touched on the application of these methods in healthcare, demonstrating how combining Machine Learning with System Dynamics could enhance predictive models in medical systems.

Key Points from the Q&A:

• Machine Learning as a Complement to System Dynamics: Mahmoud clarified that Machine Learning should be seen as a complementary tool to System Dynamics, not as a replacement. He emphasized its utility in providing data-driven insights that can inform and refine System Dynamics models.

• Bias Mitigation in Combined Approaches: In response to concerns about bias, Mahmoud discussed how integrating Machine Learning with System Dynamics could help mitigate biases from both fields. He suggested that the triangulation of data-driven insights and System Dynamics models could lead to more balanced and objective outcomes.

• Ethical Considerations in Model Design: Mahmoud acknowledged the importance of ethical considerations in the design and application of Machine Learning models, especially when used in conjunction with System Dynamics. He stressed the need for ethical frameworks to guide decision-making in these integrated approaches.

• Technical Aspects of Integration: Addressing the technicalities, Mahmoud mentioned the availability of tools and packages in programming languages like R, which facilitate the integration of traditional System Dynamics models with Machine Learning techniques.

This webinar provided a comprehensive overview of how Machine Learning can be integrated with System Dynamics to enhance model accuracy and objectivity. The key takeaway is that these two methodologies, when combined, can offer a more robust approach to understanding and solving complex problems, particularly in fields like healthcare. The Q&A session further reinforced the idea that Machine Learning and System Dynamics are not competing but are complementary tools that, when used together, can lead to more effective and ethical outcomes.

This session is organized and led by MINDS – the student-led System Dynamics Association at the University of Bergen in Norway.

Watch the recording below

About the Speaker