Decision Analysis lecture 6

Question 1

Decision Making is based on what

Answer 1

based hard evidence not intuition

Question 2

what is Business Analytics

Answer 2

Applying advanced analytical methods on data to decipher future and help make better decisions in practice

Question 3

what are Descriptive Analytics

Answer 3

Understanding past events

Question 4

what are Predictive Analytics:

Answer 4

Predicting future events

Question 5

what are Prescriptive Analytics:

Answer 5

Prescribing current/future decisions

these analytics are all about providing advice.
Attempt to quantify the effect of future decisions in order to advise on possible outcomes before the decisions are actually made

Question 6

Prescriptive analytics allows users to do what

Answer 6

“prescribe” a number of different possible actions to and guide them towards a solution

Question 7

what methods are used for prescriptive analytics

Answer 7

payoff tables/trees and decision criteria

Question 8

what are the different success criterion

Answer 8

Maximin criterion
Maximax criterion
Maximum likelihood criterion Expected value criterion

Question 9

wat is Maximin criterion:

Answer 9

alternative with highest guaranteed payoff

Question 10

is a Maximin criterion a good way to make decisions

Answer 10

sometimes referred to as “the worst-case” decision making

Question 11

what is Maximax criterion:

Answer 11

alternative with highest possible payoff (best worst case)

Question 12

what is Expected value criterion:

Answer 12

alternative with highest expected value

this is the one you use a formula for

Question 13

Expected value criterion is sometimes referred to as what

Answer 13

the risk neutral criterion

Question 14

why Expected Value

Answer 14

Law of large numbers

Question 15

what is the Law of large numbers

Answer 15

The average value of a sequence of independent and identically distributed random variables X1, X2, X3, . . . converges (“with virtual certainty”) to the expected value of the random variables

Question 16

give a summary of Maximin/maximax criterion:

Answer 16

overly pessimistic/optimistic (focus on worst/best case) ignores all information about other cases

Question 17

what is the summary of Maximum likelihood criterion:

Answer 17

focus on most likely case intuitively appealing but also ignores all information about other cases

Question 18

what is summary of Expected value criterion:

Answer 18

typically considered the criterion of choice in practice
However: expected value criterion requires (rough) knowledge of all states and their occurrence probabilities!
=⇒ use sensitivity analysis to assess parameter importance!

Question 19

Benefits of decision trees

Answer 19

Clarity of the problem:
Insight into the decision process
importance of key data

Question 20

explain Clarity of the problem:

Answer 20

Decision trees reveal the interplay of decisions and

uncertain events over time

Question 21

explain Insight into the decision process:

Answer 21

Decision trees show the optimal strategy, as well as what determines that strategy

Question 22

explain Importance of key data:

Answer 22

Sensitivity analysis allows us to determine which data is most relevant for us. This often suggests where to invest more effort into data gathering.

Question 23

what are some Extensions for the decision trees

Answer 23

Incorporation of risk attitude

Non-quantifiable consequences

Question 24

explain Incorporation of risk attitude

Answer 24

(in particular of risk aversion) =⇒ use Utility Theory

Question 25

explain Non-quantifiable consequences

Answer 25

(strategic benefits, “soft factors”) =⇒ use Multi-Criteria Decision Analysis

Question 26

go over how to make a decision tree

Answer 26

do it with practice problems