How can you balance simplicity and realism in simulation modeling?

1 Why simplicity matters

Simplicity is a desirable quality in simulation modeling because it makes your model easier to understand, communicate, and validate. A simple model can also reduce the computational time and resources needed to run the simulation. However, simplicity comes at a cost. If you oversimplify your model, you may lose important details and dynamics that affect the behavior and performance of the system. You may also introduce errors and biases that compromise the validity and reliability of your results.

2 Why realism matters

Realism is another desirable quality in simulation modeling because it makes your model more accurate, relevant, and credible. A realistic model can capture the essential features and factors that influence the system, such as variability, uncertainty, feedback, and interactions. A realistic model can also reflect the actual conditions and constraints of the system, such as physical laws, resource limits, and operational rules. However, realism comes at a cost. If you overcomplicate your model, you may increase the difficulty and uncertainty of building, testing, and analyzing the model. You may also need more data and assumptions to support the model.

3 How to balance simplicity and realism

Balancing simplicity and realism in simulation modeling is no easy task, but there are some general principles and practices that can help. Firstly, define the purpose and scope of your model, focusing on the most relevant aspects to avoid unnecessary complexity. Secondly, use abstraction and simplification techniques such as aggregation, approximation, elimination, and substitution to simplify the system without losing its essence. Thirdly, validate and verify your model with techniques such as comparing outputs with historical data or expert opinions, testing assumptions and logic, and performing sensitivity analysis. Finally, use an iterative approach to refine your model over time by collecting feedback from stakeholders and users.

4 Examples of balancing simplicity and realism

To illustrate how you can balance simplicity and realism in simulation modeling, here are some examples of successful models in various domains and contexts. For instance, a simulation model of the COVID-19 pandemic incorporated social distancing, testing, and vaccination into a simple compartmental model. Similarly, a model of a manufacturing system combined a discrete-event model with breakdowns, delays, and quality issues. Additionally, a traffic network simulation used a cellular automaton model while taking into account traffic lights, speed limits, and driver behavior. Finally, an agent-based model of a stock market simulated market trends, volatility, and bubbles.

5 Benefits of balancing simplicity and realism

Balancing simplicity and realism in simulation modeling can bring many benefits to your simulation project and outcomes, such as creating an understandable and trustworthy model to enhance communication and collaboration with stakeholders, optimizing performance and results with an efficient and effective model, and producing a flexible and robust model to adapt to changing needs and scenarios. Although not easy, following the tips and techniques in this article can help you create a simulation model that is simple enough to be manageable but realistic enough to be meaningful.

6 Here’s what else to consider

This is a space to share examples, stories, or insights that don’t fit into any of the previous sections. What else would you like to add?