One of the best books I’ve read for modelling managerial problems in socio-economic systems is “Tools for Thinking: Modelling in Management Science” by Michael Pidd.
In the following videos, I reviewed six systems modelling principles in socio-economic systems from the book.
Table of Content
- Model simple, think complicated (Cover simplicity versus complexity)
- Model development is a gradual, almost piecemeal process
- Divide and conquer! Avoid mega-models (Divide larger models into smaller components)
- Use metaphors, analogies, and similarities
- Do not fall in love with data
- The modelling process can seem chaotic and muddling through
Model simple, think complicated (Cover simplicity versus complexity)
Explore the delicate balance between simplicity and complexity in modelling. The value of embracing complexity for improved understanding and effective communication is emphasized. By navigating the complexity-simplicity spectrum, modellers can develop insightful models that strike a balance between accuracy and manageability.
Model development is a gradual, almost piecemeal process
Adopting a gradual and incremental approach to model development is essential for success. Starting with simple pieces and refining them iteratively based on feedback is widely advocated. This iterative process allows for a deeper understanding of the system being modelled and enables adjustments as new insights and data emerge. By embracing this piecemeal approach, modellers can ensure the accuracy and effectiveness of their models in supporting decision-making.
Divide and conquer! Avoid mega-models (Divide larger models into smaller components)
To effectively handle the complexity of larger models, the divide-and-conquer strategy is highly recommended. Dividing larger models into smaller, interconnected components enhances modularity and comprehensibility. This approach promotes collaboration, allows for parallel development, and facilitates the reuse of components across different models. By avoiding the pitfalls of unwieldy mega-models, decision-makers and modellers can navigate the intricacies of complex systems more efficiently and derive valuable insights.
Use metaphors, analogies, and similarities
Harnessing metaphors, analogies, and similarities in the modelling process can yield significant benefits. These cognitive tools stimulate creativity, aid in problem-solving, and enhance communication among modellers and stakeholders. By leveraging metaphors and analogies, modellers can facilitate understanding by relating complex concepts to familiar domains. Exploring similarities between different systems can lead to innovative insights and novel approaches to modelling. Embracing these techniques expands the toolbox of modellers, enabling more effective and impactful modelling outcomes.
Systems Thinking: In-Depth Exploration of its Definition, Concepts, and Tools
Systems thinking is a powerful perspective that offers a holistic way to analyze and understand various issues. In this blog, we will dive into the world of systems thinking, exploring its definition, core concepts, and tools that can aid in applying this approach to real-world problems.
Do not fall in love with data
While data is valuable, it is crucial to balance it with expert judgment, theoretical frameworks, and critical thinking in modelling. Over-reliance on data can lead to biases and limitations. A holistic perspective that considers multiple factors is essential for robust and comprehensive modelling. By incorporating diverse sources of information, modellers can navigate uncertainties and enhance the accuracy and validity of their models in practical decision-making contexts.
The modelling process can seem chaotic and muddling through
Acknowledging the challenges and uncertainties inherent in the modelling process is crucial. Embracing ambiguity, learning from failures, and iterating models based on experience are key principles for success. Modelling is often a non-linear and dynamic process that requires adaptability and resilience. By embracing the inherent chaos, modellers can navigate through complexities, refine their understanding, and ultimately develop more robust and effective models.
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P.S. Videos originally recorded in Persian but transcribed into English.
P.S. Featured image by Clint Adair on Unsplash
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