From one deterministic answer to a real comparison
Inside the model you already have.
Your model probably runs like this...
A few inputs. Your existing logic. A single set of results: an NPV, a capital profile, a production forecast, a cash flow line — whether a single number or a full time series, it's just a single, deterministic realization of those inputs.
Look at a second option, and you copy the whole thing: same duplicated logic, different inputs. A third option? Another copy. An assumption changes partway through, and you're back in every copy making the same edit in each — then updating whatever summary sheet or tab pulls the answers together, by hand, every time.
Bear Decisions runs inside a single version of your model. It generates the scenarios, holds them, and keeps them in sync as you change things.
Comparing decisions...
under a single set of conditions
So far, that's just keeping one comparison honest. Most real decisions aren't that simple.
Different types of questions lead to different approaches to decision making.
Comparison
Most teams start here. A single decision. Option A vs B vs C, side by side, for the same set of conditions. Which do you pick?
Aggregation
Scale it up. Consider multiple options each for your Assets/Projects/Decisions. Pick the combination across all of them that meets your objectives, which may not be each one's "Best".
Optimization
Go inward. Break one big decision into its smaller component choices and then find the combination that best balances cashflow and capex while staying inside budget, or whatever you're optimizing for.
But what if it is under different conditions?
A single number for price or production: you already know that's not realistic.
The problem is doing anything about it without running your own statistics project.
Run every decision option against a high, a base, and a low for every uncertain input, by hand, and tedious turns into impossible.
One input, multiple possible cases
Take production. Instead of one set of numbers, use the inputs you already have — P10, P50, P90 — each with a likelihood: not a curve to fit.
Clean lines become bands
The same comparison from before, each option now shows a range, not a single guess.
Compare full spreads
Stack it up and compare the full spread of outcomes possible for Option A against B and C, not just their base cases.
That's the core: a decision compared, and a range instead of a guess.
Everything past here is optional, depending on where you already are.
Already have insights, and stuck on landing them with stakeholders?
It has been a while since most leaders have had to weigh a range, or say out loud how much risk they're willing to carry. That conversation is real whether or not a tool is involved.
Is this decision a single commitment, or a staged one?
Often you only commit to part of a project now, and know you will learn more later. What you commit to today, and how you communicate that option value, is a different problem.