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MSS Simulation

Definition:

A simulation is computer-generated imagery (CGI) that attempts to simulate real-world conditions over time. A simulation tends to be knowledge-based and have a cognitive orientation

A simulation is a one part of a successful training program. In order to achieve results, simulator time should be combined with classroom training, hands-on training, and include an assessment program.[1]




Examples:

Fire Engineering Simulation VTRA 3D Sim Room Code 3D Simulation


Pros:

  • Cost savings – a simulation can be a cost effective way to practice making decisions in complex situations without all the resource requirements associated with exercises.
  • Simulations can be used to provide experiences that can then be used to prepare for future situations. Responders develop decision-making skills through their experiences, but more than a few incidents are necessary to develop a good experience base and simulations can fill that gap.[2]
  • Iterative nature – a simulation can be used to point out crucial decisions in the timeline of an event, allows debriefing, and provides the opportunity to try again and apply lessons learned.
  • After Action Reviews – simulations can be used to provide timely, relevant, and active learning feedback.
  • Safe environment – simulations allow participants to be exposed to a wide range of correct and incorrect decisions without physical harm or real world damage.
  • Great for building teamwork and communication.
  • The main benefits of simulation modeling are[3] (Chung, 2004)
    • Experimentation in limited time – simulations allow long and complex processes to be replicated in a compressed time period and be repeated to improve analysis
    • Reduced analytical requirements – simulation enables analysis of complex systems by practitioners, not just mathematicians and programmers. Simulation software packages embed formulas and calculations and allow practitioners to focus on analyzing a system in real time while the simulation is in progress.
    • Easily demonstrated models – most simulations allow users to dynamically animate the models being used this effectively shows how the model functions, makes the model more credible, and helps identify flaws in the model’s behavior.

Cons:

  • Simulations are data intensive and some require personnel to be trained on how to configure, update, and run them to achieve the desired results.
  • Customizations of simulations (as opposed to configuration) can be very expensive and can require ongoing work with a vendor
  • Simulations are sometimes perceived as a replacement of hands-on training and a method to achieve budget cuts, so adoption of simulation in a department or agency requires a well-thought out plan for introduction.
  • Simulation can involve significant initial upfront costs: computers, servers, licenses, custom scenarios, etc.
  • If left unchecked, users could develop bad habits or incorrect procedures based on the limitations of a simulation.
  • If a learner’s technique is incorrect, a simulation could be reinforcing bad/wrong habits.
  • The main limitations of simulation are[4] (Chung, 2004):
    • Simulation cannot give accurate results when the input data are inaccurate – otherwise known as garbage in, garbage out. Often people overlook the need to gather data to input into a simulation and underestimate or shortchange the time needed to do so.
    • Simulation cannot provide easy answers to complex answers – a complex system with many parts and interactions will need to be analyzed in pieces.
    • Simulation cannot solve problems by itself – simulation highlights potential solutions to problems that need to be implemented by management. Management buy-in is critical to success. Practitioners should beware of organizational inertia and politics as potential roadblocks to be managed.

NOTES

  • "The variables and data chosen for the model are still subjective, although the calculations suggest objectivity." and "It is often non-transparent and difficult to explain, what the model does and how it is calculated." (Kerstin Cuhls, 2005)
  • Other considerations (Chung, 2004):
    • Simulation model building can require special training
    • Simulation modeling and analysis can be costly
    • The results of simulation involve many statistics
  • The simulation modeling and analysis of different types of systems are conducted for the purposes of (Pedgen et al., 1995):
    • Gaining insight into the operation of a system
    • Developing operating or resource policies to improve system performance
    • Testing new concepts and/or systems before implementation
    • Gaining information without disturbing the actual system

 

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[1] Vehicle Driving Simulation: A Possible Solution to Vehicle Crashes, USFA Report, Parker, Patrick J., p.28.
[2] National Institute of Standards and Technology, Modeling and Simulation for Emergency Response, Workshop Report, March 4-6, 2003, Gaithersburg, MD, p. 14.
[3] Simulation Modeling Handbook: A Practical Approach, Chung, Christopher A., 1994, p.18.
[4] Simulation Modeling Handbook: A Practical Approach, Chung, Christopher A., 1994, p.19.