Modelling is an exciting and crucial process that can help us unravel the mysteries of complex systems. By creating simplified representations of these systems, we gain a unique perspective of their inner workings and uncover hidden patterns that drive emergent behaviours. These insights can be invaluable in a variety of fields, from finance and engineering to biology and ecology. By examining a system’s components and their relationships, we can gain a deeper understanding of how they interact with one another and how they influence the system as a whole. Whether we’re trying to predict the spread of a disease, optimize a manufacturing process, or design a new product, modelling can provide us with the information we need to make informed decisions and achieve our goals.
Systems modeling is a fascinating process that allows us to express a system as a model. It plays a vital role in systems thinking, helping us understand the complex nature of the object of concern. In fact, understanding a system is almost synonymous with modeling it. By exploring the system through the modeling process, we gain valuable insights. It’s important to note that the validity of the model is closely tied to the process of exploration. So, the more we refine our modeling process, the better our understanding of the system.
Modelling is a crucial aspect of the systems approach, as it serves three important functions (Takahashi, S. 2021):
- It allows for a depiction of the current state of the system. This enables analysis of the system’s components and their interrelationships.
- Modelling facilitates the creation of an optimal state that the system should strive to achieve. By establishing this objective, it becomes easier to determine how to reach it and track progress.
- Modelling highlights the problems that currently exist within the system.
By examining these issues, solutions can be generated, and the system’s overall performance can be enhanced.
Refining models for accuracy
Modelling is an iterative process. It requires refining over time as we gather new data and insights, allowing us to create more accurate representations of the system. Every modelling exercise is unique, dependent on the context, boundary selection, and purpose of the model. The modelling process enables us to capture complex relationships and dynamics within a system, providing powerful insight and foresight.
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All models are wrong
As George Box once said, “All models are wrong, some are useful” This famous quote speaks to the essence of modelling and highlights the fact that models are simplifications of complex real-world situations. In order to be useful, models must be “wrong” to a certain extent because they simplify reality. This means that the process of modelling involves deciding what to include and what to exclude. Picking the right elements, relationships, dynamics, and variables is crucial in ensuring that the model accurately represents the real-world situation it is trying to capture. Therefore, careful consideration and analysis of the components used in a model greatly determines the success of the modelling approach.
Map and territory
“A map is not the territory it represents, but, if correct, it has a similar structure to the territory, which accounts for its usefulness.” Alfred Korzybski
Territories are real-world systems with intrinsic characteristics. A single territory can have multiple models (maps), each serving a different purpose. For example, modelling a social system requires a different approach than modelling an economic system. Models enable us to identify and understand the complex patterns and relationships that drive the system, providing valuable insights for decision-making.
Tacit and explicit modelling
Patrick Hoverstadt, in his book Grammar of Systems, explains two distinct approaches to modelling – tacit and explicit. Tacit models are subconscious, personalized frameworks based on the individual’s experiences and training. Explicit models are formalized, documented systems designed for shared understanding in complex organizations (Hoverstadt, P. 2022).
Tacit models play a significant role in how teams operate, but they can lead to differences in perception among members. The difficulty in articulating these implicit models stems from their basis in past experiences and familiarity. Unfortunately, this can hinder the ability to address new, unfamiliar problems, reinforcing individual biases and ignoring potential weaknesses or blind spots. Additionally, tacit models may not be effective for managing large organizations where complexity requires a nuanced approach. By favouring incremental changes, they may limit the capacity for radical innovation. As such, it’s important for teams to explore alternative strategies to accommodate diverse perspectives and facilitate innovation.
Explicit models are an invaluable tool for promoting shared understanding among team members. By being formally documented and shared, they offer a clear structure and framework for tackling complex problems, making it easier to account for individual weaknesses and blind spots. What is perhaps most impressive about explicit models is their adaptability to new problems. Unlike models based exclusively on past experiences, explicit models are designed to be flexible and dynamic, making them well-suited to handling the complexities of large organizations. Whether you are looking for incremental or radical change, explicit models offer a clear roadmap for understanding the current state and planning changes.
Both have their unique advantages and applications. Explicit models provide a shared language for decision-making, while tacit models derive from individual observations, biases, and intuition. Understanding and utilizing both tacit and explicit models is crucial in systems thinking.
Conclusion
In conclusion, modelling is an essential part of systems thinking. By creating simplified representations of complex systems, it gives us the ability to investigate and study them. With modelling, we can identify hidden patterns and relationships that inform interventions in real-world situations. It is obvious that modelling is a crucial part of understanding systems and improving our decision-making in the real world; however, it is equally important to remember that the modelling process requires refinement over time.
In systems thinking, we use explicit models that are formally documented and shared, promoting shared understanding among team members.
Ultimately, Systems Thinking’s reliance on creating models of real-world situations reinforces its value as a powerful tool for problem-solving.
