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lso state extensively how to effectivelybalance thepredictable and the unpredictable outcome at the edge of chaoswithin the organizations.

c. Performance
In the organizational realm, simulation is often considered as decision-aid tool as it could execute the real tasks with proper standardization and validation, for instance, decision-making or diagnosis. In the decision-making phase, uncertainty and randomness are thenatural properties of the system.However, uncertainty should always be taken into consideration in the context of decision, which cannot easily be achieved in the manner of analytical formulations. Monte Carlo simulation, similar to discrete event simulation without concerning the variable of time, was introduced to mimic the possible uncertainty in the real state (Kevin, 2002). Cooper (1993) has given an example of this simulation model--to determine which to invest in from a set of project aiming at maximize some certain benefitunder the condition of limited given resource.

d. Training
Simulation is extensively used as a training method in two fields: military organizations and nuclear plants. Both of these two organizations need to reply in a timely manner to a given set of conditions, close toautomaticity. Simulation could buildvirtual reality to improve improvisationability and prepare response measuresin advance due to that the real situation is high risky, difficult and money-consuming to mimicin a physical context (Kevin, 2002).

e. Education
By studying simulation, users could obtain the knowledge about the workingmechanism of a complex system, like feedback and nonlinearity (Rushkoff, 1996).

f. Entertainment
g. Proof
Entertainment and Proof as a usage of simulation in social science realm is not common.

Simulation consists of three main schools: discrete event simulation, system dynamics and agent-based simulation (Dooley, 2002). Discrete event simulation refers to the model of the operation system having a discrete sequence of events in time. And each event occurs at a particular time which marks a change in system state (Robinson, 2004).Kevin (2002) defined discrete event simulation as a system comprisinga range of entities depending on the availability of resources and the effectiveness and triggering of events (Kevin, 2002). We willdiscuss discrete event simulation at length in the following section.
System dynamics identify the state of a system and variables which stands for the behaviors, and relates each variable to other variables according to the system rules.It studied the information-feedback from industrial activity. The outcomes may describe the influences of organizational structure, amplification, and time delays on the enterprises (Forrester, 1961). This is scientific method studying the influence of organizational structure and corporate policy on industrial growth and stability. Compared with discrete event simulation, system dynamicsfocus not only on the impact on the final target but on the interacted influences between the variables. Variables are stated by the first derivative of the state variable instead of its natural metrics. Therefore, the interacted influence can be identified between these variables. Generally speaking, with iterations, the outcomes should be more scientific compared with discrete event simulation (Kevin, 2002).

例子
Agent-b