Simulation - Banking - Lecture Slides, Slides of Banking and Finance

Download Simulation - Banking - Lecture Slides and more Slides Banking and Finance in PDF only on Docsity!

Chapter 1 – What Is Simulation? Slide 1 of 23

What is Simulation?

Chapter 1

Docsity.com

Simulation Is …

• Simulation – very broad term – methods and

applications to imitate or mimic real systems,

usually via computer

• Applies in many fields and industries
• Very popular and powerful method
• Book covers simulation in general and the Arena

simulation software in particular

• This chapter – general ideas, terminology,

examples of applications, good/bad things, kinds

of simulation, software options, how/when

simulation is used

Docsity.com

Work With the System?

• Study the system – measure, improve, design,

control

 Maybe just play with the actual system

• Advantage — unquestionably looking at the right thing  But it’s often impossible to do so in reality with the actual system
• System doesn’t exist
• Would be disruptive, expensive, or dangerous

Docsity.com

Models

• Model – set of assumptions/approximations

about how the system works

 Study the model instead of the real system … usually much easier, faster, cheaper, safer  Can try wide-ranging ideas with the model

• Make your mistakes on the computer where they don’t count, rather than for real where they do count  Often, just building the model is instructive – regardless of results  Model validity (any kind of model … not just simulation)
• Care in building to mimic reality faithfully
• Level of detail
• Get same conclusions from the model as you would from system
• More in Chapter 12 Docsity.com

Studying Logical Models

• If model is simple enough, use traditional

mathematical analysis … get exact results, lots of

insight into model

 Queueing theory  Differential equations  Linear programming

• But complex systems can seldom be validly

represented by a simple analytic model

 Danger of over-simplifying assumptions … model validity?

• Often, a complex system requires a complex

model, and analytical methods don’t apply …

what to do?

Docsity.com

Computer Simulation

• Broadly interpreted, computer simulation refers

to methods for studying a wide variety of models

of systems

 Numerically evaluate on a computer  Use software to imitate the system’s operations and characteristics, often over time

• Can be used to study simple models but should

not use it if an analytical solution is available

• Real power of simulation is in studying complex

models

• Simulation can tolerate complex models since we

don’t even aspire to an analytical solution

Docsity.com

Popularity of Simulation (cont’d.)

 1980: (A)IIE O.R. division members

• First in utility and interest — simulation
• First in familiarity — LP (simulation was second)

 1983, 1989, 1993: Longitudinal study of corporate practice

1. Statistical analysis
2. Simulation

 1989: Survey of surveys

• Heavy use of simulation consistently reported

Docsity.com

Advantages of Simulation

• Flexibility to model things as they are (even if

messy and complicated)

 Avoid looking where the light is (a morality play):

• Allows uncertainty, nonstationarity in modeling  The only thing that’s for sure: nothing is for sure  Danger of ignoring system variability  Model validity

You’re walking along in the dark and see someone on hands and knees searching the ground under a street light. You: “What’s wrong? Can I help you?” Other person: “I dropped my car keys and can’t find them.” You: “Oh, so you dropped them around here, huh?” Other person: “No, I dropped them over there.” (Points into the darkness.) You: “Then why are you looking here?” Other person: “Because this is where the light is.”

Docsity.com

The Bad News

• Don’t get exact answers, only approximations,

estimates

 Also true of many other modern methods  Can bound errors by machine roundoff

• Get random output ( RIRO ) from stochastic

simulations

 Statistical design, analysis of simulation experiments  Exploit: noise control, replicability, sequential sampling, variance-reduction techniques  Catch: “standard” statistical methods seldom work

Docsity.com

Different Kinds of Simulation

• Static vs. Dynamic

 Does time have a role in the model?

• Continuous-change vs. Discrete-change

 Can the “state” change continuously or only at discrete points in time?

• Deterministic vs. Stochastic

 Is everything for sure or is there uncertainty?

• Most operational models:

 Dynamic , Discrete-change , Stochastic

Docsity.com

Why Toss Needles?

• Buffon needle problem seems silly now, but it has

important simulation features:

 Experiment to estimate something hard to compute exactly (in 1733)  Randomness , so estimate will not be exact; estimate the error in the estimate  Replication (the more the better) to reduce error  Sequential sampling to control error — keep tossing until probable error in estimate is “small enough”  Variance reduction ( Buffon Cross )

Docsity.com

Using Computers to Simulate

• General-purpose languages (FORTRAN)

 Tedious, low-level, error-prone  But, almost complete flexibility

• Support packages

 Subroutines for list processing, bookkeeping, time advance  Widely distributed, widely modified

• Spreadsheets

 Usually static models  Financial scenarios, distribution sampling, SQC

Docsity.com

Where Arena Fits In

• Hierarchical structure

 Multiple levels of modeling  Can mix different modeling levels together in the same model  Often, start high then go lower as needed

• Get ease-of-use

advantage of

simulators without

sacrificing modeling

flexibility

Docsity.com

When Simulations are Used

• Uses of simulation have evolved with hardware,

software

• The early years (1950s-1960s)

 Very expensive, specialized tool to use  Required big computers, special training  Mostly in FORTRAN (or even Assembler)  Processing cost as high as $1000/hour for a sub-286 level machine

Docsity.com