Decision Support Tools

DECISION SUPPORT TOOLS
STUDENT ID:
[Pick the date]

Question 1
(a) There are multiple advantages associated with payoff matrix as indicated below (Hillier,
2016).
 It is possible for the decision maker to make the choice based on the underlying risk
and return preferences.
 Further, the matrix represents the expected payoff under the different states which are
not certain and use a probabilistic model to estimate the likely returns when a
particular choice is made.
The payoff matrix involves a host of steps. It begins with the identification of the likely
future states along with mentioning their respective probability of occurrence. Further, the
various choices available in terms of decision making are identified and payoffs in different
states are determined. Based on the inputs from the above stages, the requisite matrix is
formed which is formed to make decisions as per the decision strategy of the decision maker
(Taylor & Cihon, 2014).
(b) Even though the underlying person involved in making a decision can use any of the two
techniques i.e. decision tree or payoff matrix but there are certain specific scenarios
where preference to one over other is granted. In relation to decision free, this is preferred
over payoff matrix when the decision making takes place in a sequential manner. In such
scenarios, a decision tree approach is more suitable as it allows for more detailed analysis
in these scenarios (Lind, Marchal & Wathen,2016).
(c) George can take one alternative from the given three choices.
 Robot 1,
 Robot 2
 Do not buy
(1) Payoff matrix
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(2) Optimist
Maximax = Robot 1
George being optimist will choose Robot 1.
(3) Pessimist
Maximin = Do not buy
George being pessimist will not buy robot.
(4) Apply Laplace
Criterion
Maximum mean = Robot 1 or Robot 2
George will select any robot i.e. robot 1 or robot 2 because
both has same value of mean.
(5) Apply criterion of regret
Minimum of maximum opportunity cost = Robot 2
George will select robot 2.
(6) Apply expected monetary
value (EMV)
Maximum EMV = Robot 1
George will select robot 1.
(7) Expected value of perfect
information (EVPI)
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Question 2
(a) Probabilities
(b) Posterior probability
(c) EVSI and ENGSI
EVSI
ENGSI
(d) EVPI
EVPI
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