COMPUTER SIMULATION Flashcards

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1
Q

answer the question “What if?”

A

computer simulation

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2
Q

answer the question “What is?”

A

computer simulaton

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3
Q

enables studies of more complex systems because it creates observations by “moving forward” into the future

A

computer simulation

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4
Q

involves modeling the organizational system as

a set of entities evolving over time according to the availability of resources and the triggering of events.

A

Discrete event simulation

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5
Q

involves identifying the key “state” variables that define

the behavior of the system, and then relating those variables to one another through coupled, differential equations.

A

System dynamics

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6
Q

involves agents that attempt to maximize their

fitness (utility) functions by interacting with other agents and resources

A

Agent-based simulation

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7
Q

seven purpose of simulation

A

prediction, performance, training, entertainment, education, proof, theory discovery

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8
Q

By comparing different output
obtained via different structures and governing rules, researchers can infer what might
happen in the real situation if such interventions were to occur.

A

prediction

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9
Q

s a substitute for experimentation and intervention on

the actual system.

A

Simulation for prediction

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10
Q

undertaken when such experimentation is too dangerous, costly, untimely, or inconvenient

A

Simulation for prediction

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11
Q

Simulation can uncover phenomena that in turn focus theoretical attention

A

Theory discovery

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12
Q

simulation can be used to perform real tasks for an organization, such as diagnosis or decision-making.

A

performance

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13
Q

A simulation environment makes it quick, easy, and safe for users to make
decisions that mimic the decisions they (will) make in reality.

A

training

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14
Q

three different approaches to simulation

A

discrete event simulation, system dynamics, ad agent-based simulations

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15
Q

are best used when the organizational system
under study can be adequately characterized by variables and corresponding
states, and events occur that change the value of these variable states in some
rule-oriented but stochastic manner.

A

Discrete event simulation

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16
Q

not appropriate

when state variables interact with one another and change on a continuous basis,

A

Discrete event simulation

17
Q

best fit for situations
where the variables in question are numerous, and can be related to one another in
terms of how their rates of change interact with one another.

A

System dynamics

18
Q

tend to treat systems rather mechanistically

A

System dynamics

19
Q

a “top-down” modeling
approach, and thus requires fairly extensive knowledge about how the state
variables of the system interact with one another.

A

System dynamics

20
Q

best fit for situations when the organizational
system is best modeled as a collection of agents who interpret the world around
themselves and interact with one another via schema

A

agent-based simulations

21
Q

emphasize change in agents’ schema via learning and adaptation, and also
highlight the phenomena of emergent

A

agent-based simulations

22
Q

considered “bottom-up” models
in that one describes individual agents and their patterns of connectivity and
interaction

A

agent-based simulations