CoffeeTime Probability Theory in Decision Making simulation
Run the “Probability Theory in Decision Making”
simulation and then answer the following questions, in short answer format:
a. CoffeeTime is considering selling juices along with its
other products.
States of Nature
High Sales Med.
Sales Low Sales
A(0.2) B(0.5) C(0.3)
A1 (sell
juices) 3000 2000 -6000
A2 (don’t
sell juices) 0 0 0
The probabilities shown above represent the states of nature
and the decision maker’s (e.g., manager) degree of uncertainties and personal
judgment on the occurrence of each state. What is the expected payoff for
actions A1 and A2 above? What would be your recommendation? Interpret the
results based on practical considerations.
b. Bayes and empirical Bayes (EB) methods structure
combining information from similar components of information and produce
efficient inferences for both individual components and shared model
characteristics. For example, city-specific information on the profits involved
in selling a particular brand of coffee in Mumbai might be unavailable. How
could CoffeeTime “borrow information” from adjacent cities or other
countries to employ Bayesian logic?
Finally, what additional strategy (or variation on a given
strategy) would you recommend to the key decision maker in the simulation to
solve the challenge given? Prepare a 350-word memo to the simulation’s key
decision maker advocating your recommendation
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