Deterministic System refers to a system in which its future state is fully determined by its current state and the set of rules or equations that govern its behavior. In other words, the system’s evolution over time is predictable and follows a well-defined path. There is no randomness or uncertainty in the evolution of a deterministic system.
Key characteristics of deterministic systems include:
- Predictability: Given the initial conditions and the governing rules, the future state of the system can be precisely predicted. There is no inherent randomness or chance involved.
- Repeatability: If the system is started from the same initial conditions and follows the same rules, it will always evolve in the same way. This repeatability is a fundamental feature of deterministic systems.
- No Randomness: Unlike stochastic or probabilistic systems, where randomness plays a role in the system’s behavior, deterministic systems do not involve random elements. Every aspect of the system’s behavior is determined by its initial conditions and governing equations.
Deterministic systems are commonly encountered in classical physics, where Newtonian mechanics, for example, provides deterministic equations of motion. However, it’s important to note that not all systems in the real world are purely deterministic. Quantum mechanics, for instance, introduces an element of inherent randomness at the microscopic level.
In the realm of decision support systems (DSS), deterministic models are often contrasted with stochastic models. Deterministic models assume that all parameters and inputs are known with certainty, and there is no randomness in the decision-making process. Stochastic models, on the other hand, incorporate randomness or uncertainty into the modeling process.
Deterministic models can be valuable in situations where the system under consideration is well-understood, and the inputs are known with a high degree of certainty. However, in many real-world scenarios, uncertainty is inevitable, and stochastic models may be more appropriate for capturing the variability and unpredictability inherent in certain processes.