The word simulation refers to any procedure that is meant to imitate a real-life system. Simulations are especially useful in examining situations that are too complex, too difficult, or too costly to explore in the real world. The computer is often used for this purpose because it is able to efficiently model systems and process data. The phrase computer simulation is a broad rubric for a range of different types of methodologies; the following are their general forms.
Monte Carlo Simulation
In a Monte Carlo simulation, values for uncertain variables are generated by the computer to reproduce information found in the real world. Named for the city of Monte Carlo, Monaco (where the primary attractions are games of chance at gambling casinos), a Monte Carlo simulation generates data pseudorandomly to explore hypothesized models. Much like the random behavior in games of chance, a Monte Carlo simulation selects values at random to simulate a variable. For example, when you roll a die, you know that a number from one to six will come up, but you don’t know what number will come up for any particular roll. In much the same way, a Monte Carlo simulation works by first defining the possible values that simulated data can take as the same values found in the real world and then using that definition to generate random numbers. In this way, any number of variables that have a known range of values but an uncertain value for any particular time or event (e.g., interest rates, staffing needs, stock prices, inventory, phone calls per minute) can be modeled. In a typical Monte Carlo simulation, behavioral processes are entirely simulated by the computer.
Microworld simulations have a higher level of realism. Microworld simulations are complex, computer-generated situations used in controlled experiments that are designed to study decision making. Micro-world simulations represent a compromise between experimental control and realism and enable researchers to conduct experimental research within a dynamic, complex decision-making situation. In a typical microworld simulation, the situation is generated with a moderate degree of fidelity and behavioral processes are examined as humans navigate through it. The simulation is typically only unidimensional— that is, participants are instructed to make decisions that are cognitively complex but that do not invoke a range of senses (e.g., visual, aural, olfactory, tactile, and proprioceptive).
Virtual Reality Simulation
At the most realistic level, a virtual reality (VR) simulation is defined as a computer-simulated, multisensory environment in which a perceiver—the user of the VR computer technology—experiences telepresence. Telepresence is defined as feeling present in an environment that is generated by a communication medium such as a computer. In the context of VR, telepresence occurs when the VR user loses awareness of being present at the site of the human-computer interface and instead feels present or fully immersed in the VR environment. Thus, a successful VR simulation reproduces the experience of reality with a high degree of accuracy so that behavioral processes can be examined as humans navigate through the simulated environment. The simulation is typically multidimensional—that is, the best VR simulations attempt to invoke the full range of participants’ senses (i.e., visual, aural, olfactory, tactile, and proprioceptive).
Simulation Examples from the Literature
Computer simulations have been used to explore a multitude of real-world situations. What follows are a number of examples, broken down by simulation type, which may help to make the exposition more concrete.
Monte Carlo Simulations
Monte Carlo simulations have been used to investigate such phenomena as faking on personality inventories, the effect of forced distribution rating systems on workforce potential, adverse impact in selection, statistical properties of various indexes, and withdrawal behaviors, to name just a few.
Microworld simulations have been used to study a number of situations, including a sugar production factory, a fire chief’s job, a beer game, and a water production plant. These microworlds vary along four dimensions: (a) dynamics, that is, the system’s state at time t depends on the state of the system at time t – 1; (b) complexity, or the degree to which the parts interconnect, making it difficult to understand or predict system behavior; (c) opaqueness, or the invisibility of some parts of the system; and (d) dynamic complexity, or the effect of feedback structures on a decision maker’s ability to control a dynamic system.
Virtual Reality Simulations
Virtual reality simulations have been used to create virtual environments to assess large-scale spatial abilities; to model responses to a fire; and to prepare trainees for job experiences that normally would have high costs (e.g., flying an airplane), the risk of costly damage to equipment (e.g., landing a plane on an aircraft carrier), or the potential for injuries to the trainee (e.g., training in a race car).
Advantages and Disadvantages Of Computer Simulations
There are trade-offs with any methodology. The major advantage of computer simulations is that they are particularly well-adapted for situations in which it would be difficult, because of cost, safety, or validity, to examine a particular phenomenon in a real-life situation. With a computer simulation, any one of a number of naturally occurring parameters can be manipulated in a controlled laboratory setting many times without endangering participants, spending large sums of money, or resorting to correction formulas for participants who drop out.
The major disadvantage of computer simulations is their lack of external generalizability—that is, the degree to which the results of the computer simulation apply to actual situations and behavior in real life. However, external generalizability can be enhanced in several ways:
- When conducting Monte Carlo studies and designing microworlds, choose parameter estimates sensibly (e.g., from prior empirical studies).
- When conducting VR simulations, ensure that VR environments invoke maximal vividness and interactivity.
- Gamberini, L., Cottone, P., Spagnolli, A., Varotto, D., & Mantovani, G. (2003). Responding to a fire emergency in a virtual environment: Different pattern of actions for different situations. Ergonomics, 46, 842-858.
- Gonzalez, C., Vanyukov, P., & Martin, M. K. (2005). The use of microworlds to study dynamic decision making. Computers in Human Behavior, 21, 273-286.
- Scullen, S. E., Bergey, P. K., & Aiman-Smith, L. (2005). Forced distribution rating systems and the improvement of workforce potential: A baseline simulation. Personnel Psychology, 58, 1-32.
- Seitz, S. T., Hulin, C. L., & Hanisch, K. A. (2000). Simulating withdrawal behaviors in work organizations: An example of a virtual society. Nonlinear Dynamics, Psychology, and Life Sciences, 4, 33-65.
- Waller, D. (2005). The WALKABOUT: Using virtual environments to assess large-scale spatial abilities. Computers in Human Behavior, 21, 243-253.