Page 92 - DCAP601_SIMULATION_AND_MODELING

Simulation and Modelling

Notes computational molecular dynamics; see Monte Carlo method in statistical physics. In
experimental particle physics, these methods are used for designing detectors, understanding
their behavior and comparing experimental data to theory.
Design and Visuals

Monte Carlo methods have also proven efficient in solving coupled integral differential equations
of radiation fields and energy transport, and thus these methods have been used in global illumination
computations which produce photorealistic images of virtual 3D models, with applications in video
games, architecture, design, computer generated films, special effects in cinema.

Finance and Business

Monte Carlo methods in finance are often used to calculate the value of companies, to evaluate
investments in projects at corporate level or to evaluate financial derivatives. The Monte Carlo
method is intended for financial analysts who want to construct stochastic or probabilistic
financial models as opposed to the traditional static and deterministic models for its use in the
insurance industry.

Telecommunications

When planning a wireless network, design must be proved to work for a wide variety of
scenarios that depend mainly on the number of users, their locations and the services they want
to use. Monte Carlo methods are typically used to generate these users and their states. The
network performance is then evaluated and, if results are not satisfactory, the network design
goes through an optimization process.

Games

Monte Carlo methods have recently been applied in game playing related artificial intelligence
theory. Most notably the game of Go has seen remarkably successful Monte Carlo algorithm
based computer players. One of the main problems that this approach has in game playing is
that it sometimes misses an isolated, very good move. These approaches are often strong
strategically but weak tactically, as tactical decisions tend to rely on a small number of crucial
moves which are easily missed by the randomly searching Monte Carlo algorithm.
Monte Carlo Simulation versus “What If” Scenarios

The opposite of Monte Carlo simulation might be considered deterministic using single-point
estimates. Each uncertain variable within a model is assigned a “best guess” estimate. Various
combinations of each input variable are manually chosen (such as best case, worst case, and most
likely case), and the results recorded for each so-called “what if” scenario.
By contrast, Monte Carlo simulation considers random sampling of probability distribution
functions as model inputs to produce hundreds or thousands of possible outcomes instead of a
few discrete scenarios. The results provide probabilities of different outcomes occurring.

Example: A comparison of a spreadsheet cost construction model run using traditional
“what if” scenarios, and then run again with Monte Carlo simulation and Triangular probability
distributions shows that the Monte Carlo analysis has a narrower range than the “what if”
analysis. This is because the “what if” analysis gives equal weight to all scenarios.

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