Python Tutorial: Using simulation for decision-making
Key Takeaways
Uses simulation for decision-making in Python
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now let's see how we can use simulation for decision-making here is a more generalized version of the simple simulation workflow you used in the previous exercise steps one and two involves defining the outcomes for random variables and assigning probabilities we could also have constraints entering the model in this step step three involves defining the relationship between model parameters in step four we repeatedly sample from the distributions of a and B to generate outcomes finally in step five we analyze outcomes simulations let us ask nuanced questions of the model questions which might not necessarily have easy analytical solutions this is because we can easily modify model inputs and see how the final outcomes are affected for example we might want to ask how the outcome will change is the probability distribution of B changes in this case we would just simulate a new set of outcomes after changing B let's see what happens if we do that we might find that as a result of the change in B the distribution of outcomes has changed significantly this might help us decide for instance that we should not have a particular stock in our portfolio it helps us see how sensitive our model is to changes in be another case where we use simulations is when we might want to see what values of a particular input get us the desired output for instance we might want to find the lowest value of the constant C for which the mean of the outcome distribution is five in this case all we need to do is just iteratively keep changing the value of the constant C and record the outcomes we will do this many times and keep recording the outcomes finally we can choose the value of C where the mean of the outcome distribution is five using simulation for making decisions is as easy as that such a simulation could for example help us decide the right price at which it is still profitable to invest in a loan now let's take all these ideas and work through some exercises where you will evaluate a lot rating
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Now let's see how we can use simulation for decision-making.
Here is a more generalized version of the simple simulation workflow you used in the previous exercise. Steps 1 and 2 involve defining the outcomes for random variables and assigning probabilities. We could also have constants entering the model in this step. Step 3 involves defining the relationship between model parameters. In step 4, we repeatedly sample from the distributions of A & B to generate outcomes. Finally in step 5, we analyze outcomes.
Simulations let us ask nuanced questions of the model, questions which might not necessarily have easy analytical solutions. This is because we can easily modify model inputs and see how the final outcomes are affected. For example, we might want to ask how the outcome will change if the probability distribution of B changes. In this case, we would just simulate a new set of outcomes after changing B. Let's see what happens if we do that.
We might find that as a result of the change in B, the distribution of outcomes has changed significantly. This might help us decide, for instance, that we should not have a particular stock in our portfolio. It helps us see how sensitive out model is to changes in B.
Another case where we use simulations is when we might want to see what values of a particular input get us the desired output. For instance, we might want to find the lowest value of the constant C for which the mean of the outcome distribution is 5.
In this case, all we need to do is just iteratively keep changing the value of the constant C and record the outcomes. We will do this many times and keep recording the outcomes.
Finally, we can choose the value of C where the mean of the outcome distribution is 5. Using simulation for m
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