In: Finance
1. What is the main goal in decision analyses. Be specific and complete using terminology from the course.
2. Why perform sensitivity analysis? Of what use is sensitivity analysis where good probability estimates are difficult to obtain?
*Please use different answers from the chegg because my friend already use it.
Answer Goals of decision analysis
There are many layers of answers to this question. At one level, the focus of decision analysis is to provide clarity of action. It does this by helping you bring together the six elements of decision quality.
At a deeper level, the purpose of decision analysis is to deliver equanimity. Built in to the very math of decision analysis is the notion of detachment. While economics and statistics would have us call the probability-weighted average of a deal the “expected value,” decision analysis offers an entirely different term - “certain equivalent.” It is that amount of money for which you'd be indifferent between having the money or having the deal. It has nothing to do with the English word “expectation.” Those who are able to adopt the essence of these ideas end up reprogramming their minds to believe that they are ahead by whatever they calculated as the worth of the deal before the outcome is evident. That leads to peace of mind - you have done the best you could, and you have now freed yourself to make the next decision as well as you can. I may add that all of this rests on the foundational principle of DA - you cannot judge a decision from the outcome.
Going to an even deeper level, the purpose of decision analysis is to take complexity (in the decision) off the table so that you can focus on who you want to be. It is not about the math, although there is some very powerful math at your disposal if the decision is complex enough to require the math. This is the ultimate opportunity every human being has through their decisions - to shape themselves through their actions.
Sensitivity Analysis
It helps in analyzing how sensitive the output is, by the changes in one input while keeping the other inputs constant.
Sensitivity analysis works on the simple principle: Change the model and observe the behavior.
The parameters that one needs to note while doing the above are:
A) Experimental design: It includes combination of parameters that are to be varied. This includes a check on which and how many parameters need to vary at a given point in time, assigning values (maximum and minimum levels) before the experiment, study the correlations: positive or negative and accordingly assign values for the combination.
B) What
tfo vary:The different parameters that can be chosen
to vary in the model could be:
a) the number of activities
b) the objective in relation to the risk assumed and the profits
expected
c) technical parameters
d) number of constraints and its limits
C) What to
observe:
a) the value of the objective as per the strategy
b) value of the decision variables
c) value of the objective function between two strategies
adopted
Measurement of sensitivity analysis
Below are mentioned the steps used to conduct sensitivity analysis:
This process of testing sensitivity for another input (say cash flows growth rate) while keeping the rest of inputs constant is repeated till the sensitivity figure for each of the inputs is obtained. The conclusion would be that the higher the sensitivity figure, the more sensitive the output is to any change in that input and vice versa.
Methods of Sensitivity Analysis
There are different methods to carry out the sensitivity analysis:
There are mainly two approaches to analyzing sensitivity:
Local sensitivity analysis is derivative based (numerical or analytical). The term local indicates that the derivatives are taken at a single point. This method is apt for simple cost functions, but not feasible for complex models, like models with discontinuities do not always have derivatives.
Mathematically, the sensitivity of the cost function with respect to certain parameters is equal to the partial derivative of the cost function with respect to those parameters.
Local sensitivity analysis is a one-at-a-time (OAT) technique that analyzes the impact of one parameter on the cost function at a time, keeping the other parameters fixed.
Global sensitivity analysis is the second approach to sensitivity analysis, often implemented using Monte Carlo techniques. This approach uses a global set of samples to explore the design space.
The various techniques widely applied include:
Through the sensitivity index one can calculate the output % difference when one input parameter varies from minimum to maximum value.
Using Sensitivity Analysis for decision making
One of the key applications of Sensitivity analysis is in the
utilization of models by managers and decision-makers. All the
content needed for the decision model can be fully utilized only
through the repeated application of sensitivity analysis. It helps
decision analysts to understand the uncertainties, pros and cons
with the limitations and scope of a decision model.
Most if not all decisions are made under uncertainty. It is the
optimal solution in decision making for various parameters that are
approximations. One approach to come to conclusion is by replacing
all the uncertain parameters with expected values and then carry
out sensitivity analysis. It would be a breather for a decision
maker if he / she has some indication as to how sensitive will the
choices be with changes in one or more inputs.
Uses of Sensitivity Analysis
Conclusion
Sensitivity analysis is one of the tools that help decision makers with more than a solution to a problem. It provides an appropriate insight into the problems associated with the model under reference. Finally the decision maker gets a decent idea about how sensitive is the optimum solution chosen by him to any changes in the input values of one or more parameters.