Business Analytics Exam 1 (OSU BUSMGT 2321)

define the problem

Decision Analysis step 1

list all alternatives (decision options)

Decision Analysis step 2

identify all possible states of nature (outcomes)

Decision Analysis step 3

identify payoffs (gain/loss from each combo of alternative and state of nature)

Decision Analysis step 4

choose modeling technique

Decision Analysis step 5

expected monetary value

EMV is an acronym for...

Expected Value of Perfect Information

EVPI

EVwPI - EMVmax

EVPI =

Expected Value with Perfect Information

EVwPI

optimistic

Maximax criteria is considered....

pessimistic

Maximin criteria is considered...

Monday morning Quarterback

Minimax Regret is considered as a ............................ strategy.

Equally likely criteria

a decision model where the probabilities of each state of nature are unknown, and the decision maker assumes the probability for each of the states are equal

infeasible solution

a solution to a linear program where NO points satisfy all of the constraints simultaneously

unbounded solution

a solution to a linear program where X1 and/or X2 are unconstrained; objective function value becomes more optimal as the variable approaches infinity

unique optimal solution

a solution to a linear program where only one of the corner points yields the optimal objective function value

multiple optimal solution

a solution to linear program where two corner yield the same, optimal objective function value (all other points on a line between these corner points are also optimal solutions)

EMV

a weighted average that is calculated by multiplying every payoff of a decision by the probabilities of each state of nature occurring

Equal Opportunity Loss

a weighted average that is calculated by multiplying every regret of a decision by the probabilities of each state of nature occurring

formulate

decision modeling step 1

solution

decision modeling step 2

interpret

decision modeling step 3

squares

decision tree models show decisions as .....................

circles

decision tree models show states of nature as ........................

operational (decisions)

decisions that take a limited amount of time and have a small consequence for an incorrect decision

strategic (decisions)

decisions that take a long time and has significant consequences for an incorrect decision (e.g. mergers/acquisitions)

tactical (decisions)

decisions that take a significant amount of time and can have a high consequence for the incorrect decision (e.g. forecasting production)

x1

horizontal axis variable

minimax regret criteria

identify maximum regret for each decision. then select the decision with the minimum regret.

Maximax Criteria

identify the maximum payoff for each decision. then select the decision with the maximum payoff

Maximin Criteria

identify the minimum payoff for each decision. then select the decision with the maximum payoff

constraints

limitations used in modeling to ensure the model conforms to reality

aggressive

maximax criteria indicates ....................... behavior

conservative

maximin criteria indicates ...................... behavior

competitive

minimax regret criteria indicates ..................... behavior

identify the best outcome for each state of nature

minimax regret table step 1

determine regret for each combination

minimax regret table step 2

identify max regret for each option

minimax regret table step 3

select decision with minimum regret (from maximums in step 3)

minimax regret table step 4

certainty

one of the four assumptions of linear decision modeling, all inputs to the model are correct/true

additivity

one of the four assumptions of linear decision modeling, all inputs to the model are cumulative (can be totaled)

proportionality

one of the four assumptions of linear decision modeling, all inputs to the model do NOT change regardless of production level

divisibility

one of the four assumptions of linear decision modeling, the optimal solution can have a decimal regardless if its impossible to produce a fraction of a unit

indifference point

quantity where decisions change

break-even point

quantity where π = 0

define, develop, & data

steps in formulate phase of decision modeling

analyze, sensitivity, & implement

steps in interpret phase of decision modeling

develop & test

steps in solution phase of decision modeling

shadow price

the change in the objective function value when the RHS of a constraint is increased by 1 unit

objective function

the equation you are trying to optimize (max./min.) in a model

certainty, proportionality, additivity, divisibility

the four assumptions of decision modeling with a linear program

decision tree

the model best suited to implement the concept of time

sensitivity

the optimal solution's ...................... is a measure of how much the objective function coefficients or RHS of the constraints can be changed before the optimal solution changes

corner points

the points where the constraints intersect to create the feasible region; the optimal solution to a linear program always occurs at one of these points

game theory

the science of logical decision making (particularly against a competitor)

x2

vertical axis variable