I am creating a profitability model for a proposed chemical manufacturing project and some of the parameters that go into this model are subject to uncertainty.
e.g. The price of Styrene (a feedstock), or the cost of electricity, cost of coal etc.
The goal is to not only estimate the profitability of the project but include some information about the variability of the estimate. Perhaps 95% uncertainty intervals.
One way to capture this is to actually ask the relevant experts. e.g. The key coal suppliers. Or people from the electricity distribution business.
I know this is not foolproof but it's a start to quantifying the uncertainty.
My question: What's the best way to capture information from these "statistical laymen" so that it can be translated into some sort of useful measure of variation of the underlying price?
e.g.
I could ask:
- What do you foresee as the most likely price of Styrene one year from now?
- What do you foresee as the standard distribution of Styrene price variation? (but that does not make much sense to most people)
- What do you feel the lowest or highest price of styrene could be?
etc.
My hope is that this info can be somehow mapped to a distribution of likely values of that parameter & then drawing from these distributions (hopefully uncorrelated) I can generate some sort of distribution of the final profitability.
Even if I assume a normal distribution for all parameters, what are good framings of questions to ask a non-statistician whose answers can then be mapped to a mean & standard deviation of the distribution to use?
Any thoughts how to go about this?