How do you balance the trade-off between accuracy and simplicity in uncertainty analysis?

1 Know your purpose

Before you start your uncertainty analysis, you need to define your purpose and scope. What are the main questions you want to answer? Who are the intended users and stakeholders of your analysis? How will they use the information and what level of detail do they need? Your purpose and scope will help you determine the type and level of uncertainty analysis that is suitable for your case. For example, if you are doing a preliminary screening of options, you may use a simple sensitivity analysis to identify the key factors that affect your outcomes. If you are doing a detailed evaluation of alternatives, you may use a more complex probabilistic analysis to estimate the likelihood and magnitude of different scenarios.

2 Choose your methods and tools

There are many methods and tools available for uncertainty analysis, ranging from qualitative to quantitative, from deterministic to stochastic, from spreadsheet-based to software-based. Each method and tool has its own advantages and disadvantages, depending on the complexity, uncertainty, and data availability of your problem. You need to choose the methods and tools that best fit your purpose, scope, and resources. For example, if you have a lot of data and a well-defined model, you may use a Monte Carlo simulation to generate a distribution of possible outcomes. If you have limited data and a vague model, you may use a scenario analysis to explore a few plausible outcomes.

3 Simplify but don't oversimplify

One of the challenges of uncertainty analysis is to simplify your problem enough to make it manageable, but not so much that you lose the essential features and uncertainties. You need to strike a balance between accuracy and simplicity, based on your purpose, scope, and resources. You can simplify your problem by making reasonable assumptions, using representative data, aggregating variables, or using simplified models. However, you should also avoid oversimplifying your problem by ignoring important uncertainties, using unrealistic data, masking variability, or using inappropriate models. You should always check the validity and sensitivity of your simplifications and document them clearly.

4 Communicate your results

The final step of uncertainty analysis is to communicate your results to your users and stakeholders. You need to present your results in a clear, concise, and transparent way, using appropriate formats, visuals, and language. You should also explain the sources, types, and levels of uncertainty in your analysis, and how they affect your conclusions and recommendations. You should also acknowledge the limitations and assumptions of your analysis, and provide guidance on how to interpret and use the results. You should also invite feedback and questions from your users and stakeholders, and update your analysis as needed.