A friend of mine just mentioned to me that the following points could be useful in the context of this discussion.
What DMDU researchers are usually doing is to use uniform probability distributions for all parameters when exploring future scenarios. This approach allows for a more even exploration of the plausible space, rather than being overly concerned with subjective probabilities, which may lead to sampling some regions of input-output space less densely and potentially missing decision-relevant outcomes. The benefit of using uniform probability distributions is that it can help to avoid compounding uncertainties in a way that can lead to biased results. When you use a uniform distribution, you assume that all values are equally likely within the range of possible outcomes. This approach can help to ensure that your exploration of the future is more comprehensive and that you are not overlooking important possibilities. Of course, there may be cases where subjective probabilities are essential, such as when there is prior knowledge or data that strongly suggests certain outcomes are more likely than others. In such cases, I’d say that it may be appropriate to incorporate those probabilities into the model.
Also, this paper by James Derbyshire on probability-based versus plausibility-based scenarios might be very relevant. The underlying idea of plausibility-based scenarios is that any technically possible outcome of a model is plausible in the real world, regardless of its likelihood (given that the model has been well validated). This approach recognizes that complex systems, especially those with deep uncertainties, can produce unexpected outcomes that may not have been considered in a traditional probability-based approach. When making decisions under deep uncertainty, it’s important to take seriously the range of technically possible but seemingly unlikely outcomes. This is where the precautionary principle comes in (which advocates for taking action to prevent harm even when there is uncertainty about the likelihood of that harm). By including these “fat tail” outcomes in our analysis, we are able to identify and prepare for potentially severe outcomes that may have significant consequences. Additionally, nonlinearities can further complicate the relationship between probability and plausibility. In some cases, even a small change in initial conditions or inputs can lead to drastic differences in the final outcome. By exploring the range of plausible outcomes rather than just the most likely outcomes, we can better understand the potential consequences of our decisions and be more prepared to mitigate risks and respond to unexpected events.
I’m not sure I disagree with any of this, and in fact if I understood correctly, the point about using uniform probability distributions is basically what I was suggesting: it seems like you can always put 1/n instead of a “?” which just breaks your model. I agree that sometimes it’s better to say “?” and break the model because you don’t always want to analyze complex things on autopilot through uncertainty (especially if there’s a concern that your audience will misinterpret your findings), but sometimes it is better to just say “we need to put something in, so let’s put 1/n and flag it for future analysis/revision.”
A friend of mine just mentioned to me that the following points could be useful in the context of this discussion.
What DMDU researchers are usually doing is to use uniform probability distributions for all parameters when exploring future scenarios. This approach allows for a more even exploration of the plausible space, rather than being overly concerned with subjective probabilities, which may lead to sampling some regions of input-output space less densely and potentially missing decision-relevant outcomes. The benefit of using uniform probability distributions is that it can help to avoid compounding uncertainties in a way that can lead to biased results. When you use a uniform distribution, you assume that all values are equally likely within the range of possible outcomes. This approach can help to ensure that your exploration of the future is more comprehensive and that you are not overlooking important possibilities. Of course, there may be cases where subjective probabilities are essential, such as when there is prior knowledge or data that strongly suggests certain outcomes are more likely than others. In such cases, I’d say that it may be appropriate to incorporate those probabilities into the model.
Also, this paper by James Derbyshire on probability-based versus plausibility-based scenarios might be very relevant. The underlying idea of plausibility-based scenarios is that any technically possible outcome of a model is plausible in the real world, regardless of its likelihood (given that the model has been well validated). This approach recognizes that complex systems, especially those with deep uncertainties, can produce unexpected outcomes that may not have been considered in a traditional probability-based approach. When making decisions under deep uncertainty, it’s important to take seriously the range of technically possible but seemingly unlikely outcomes. This is where the precautionary principle comes in (which advocates for taking action to prevent harm even when there is uncertainty about the likelihood of that harm). By including these “fat tail” outcomes in our analysis, we are able to identify and prepare for potentially severe outcomes that may have significant consequences. Additionally, nonlinearities can further complicate the relationship between probability and plausibility. In some cases, even a small change in initial conditions or inputs can lead to drastic differences in the final outcome. By exploring the range of plausible outcomes rather than just the most likely outcomes, we can better understand the potential consequences of our decisions and be more prepared to mitigate risks and respond to unexpected events.
I hope that helps!
I’m not sure I disagree with any of this, and in fact if I understood correctly, the point about using uniform probability distributions is basically what I was suggesting: it seems like you can always put 1/n instead of a “?” which just breaks your model. I agree that sometimes it’s better to say “?” and break the model because you don’t always want to analyze complex things on autopilot through uncertainty (especially if there’s a concern that your audience will misinterpret your findings), but sometimes it is better to just say “we need to put something in, so let’s put 1/n and flag it for future analysis/revision.”
Yes, I think that in this sense, it fits rather well! :)