Monte Carlo Simulation

Question 1

base-case scenario

Answer 1

Output resulting from the most likely values for the random variables of a model.

Question 2

Best case scenario

Answer 2

Output resulting from the best values that can be expected for the random variables of a model.

Question 3

continuous probability distribution

Answer 3

A probability distribution for which the possible values for a random variable can take any value in an interval or collection of intervals. An interval can include negative and positive infinity.

Question 4

Controllable input

Answer 4

Input to a simulation model that is selected by the decision maker.

Question 5

discrete probability distribution

Answer 5

A probability distribution for which the possible values for a random variable can take on only specified values.

Question 6

Discrete event simulation

Answer 6

A simulation method that describes how a system evolves over time by using events that occur at specific points in time.

Question 7

Monte Carlo simulation

Answer 7

A simulation method that uses repeated random sampling to represent uncertainty in a model representing a real system and that computes the values of model outputs.

Question 8

Probability distribution

Answer 8

A description of the range and relative likelihood of possible values of a random variable (uncertain quantity).

Question 9

Random variable

Answer 9

Input to a simulation model whose value is uncertain and described by a probability distribution.

Question 10

risk analysis

Answer 10

The process of evaluating a decision in the face of uncertainty by quantifying the likelihood and magnitude of an undesirable outcome.

Question 11

simulation optimization

Answer 11

The process of applying optimization techniques to identify optimal (or near-optimal) values of the decision variables in a simulation model.

Question 12

Validation

Answer 12

The process of determining that a simulation model provides an accurate representation of a real system.

Question 13

Verification

Answer 13

The process of determining that a computer program implements a simulation model as it is intended.

Question 14

What-if analysis

Answer 14

A trial-and-error approach to learning about the range of possible outputs for a model. Trial values are chosen for the model inputs (these are the what-ifs) and the value of the output(s) is computed.

Question 15

Worst case scenario

Answer 15

Output resulting from the worst values that can be expected for the random variables of a model.