In mathematical modeling, deterministic simulations contain no random variables and no degree of randomness, and consist mostly of equations, for example difference equations. These simulations have known inputs and they result in a unique set of outputs. Contrast stochastic (probability) simulation, which includes random variables. In mathematical modeling, deterministic simulations contain no random variables and no degree of randomness, and consist mostly of equations, for example difference equations. These simulations have known inputs and they result in a unique set of outputs. Contrast stochastic (probability) simulation, which includes random variables. Deterministic simulation models are usually designed to capture some underlying mechanism or natural process. They are different from statistical models (for example linear regression) whose aim is to empirically estimate the relationships between variables. The deterministic model is viewed as a useful approximation of reality that is easier to build and interpret than a stochastic model. However, such models can be extremely complicated with large numbers of inputs and outputs, and therefore are often noninvertible; a fixed single set of outputs can be generated by multiple sets of inputs. Thus taking reliable account of parameter and model uncertainty is crucial, perhaps even more so than for standard statistical models, yet this is an area that has received little attention from statisticians. Deterministic simulations in scientific research are used in various studies about population fields, climate development, and pollution, engineering, chemistry and policy-making. Deterministic simulations have received attention in statistical literature under the general topic of computer experiments. Computer experiments simulate complex system which requires a number of inputs. Use of a stochastic system is much cheaper but also inaccurate and simplifying. It is necessary to translate models into computer recognizable formats. The modeler must decide if whether to program the model in a simulation language such as GPSS/H or to use special purpose simulation software: Arene – discrete event simulator has also academic version CSIM – CSIM is a re-usable general purpose discrete-event simulation environment for modeling complex systems of interacting elements. It contains hierarchical block diagram tools and extensive model libraries covering several domains. CSIM can be used for modeling: agent-based systems, logistics, wireless networks, computer networks... Dynare – when the framework is deterministic, can be used for models with the assumption of perfect foresight. The purpose of the simulation is to describe the reaction in anticipation of, then in reaction to the shock, until the system returns to the old or to a new state of equilibrium. Janus – Janus is an interactive simulation war game portraying realistic events during multi-sided combat. It uses digitized terrain effecting line of sight and movement, depicting contour lines, roads, rivers, vegetation and urban areas. It has the capability to be networked with other systems, in order to simulate a war game with multiple sides. Modsaf (Modular Semi-Automated Forces) is a set of software modules and applications used to construct Advanced Distributed Simulation (ADS) and Computer Generated Forces (CGF) applications. ModSAF modules and applications let a single operator create and control large numbers of entities that are used for realistic training, test, and evaluation on the virtual battlefield. ModSAF contains entities that are sufficiently realistic resulting in the user not being aware that the displayed vehicles are being maneuvered by computers, rather than human crews. These entities, which include ground and air vehicles, dismounted infantry (DI), missiles, and dynamic structures, can interact with each other and with manned individual entity simulators to support training, combat development experiments, and test of evaluation studies.