Building a model that mimics a real-world system generally follows a series of stages: from conceptual models to mathematical models and, finally, simulation models. In model development, system analysis is a process whereby a real-world system is simplified by dividing it into simpler, more manageable parts. A conceptual model captures the components, variables and interactions of a system, and provides a useful way of thinking about the trade-offs between abstraction and representativeness of real-world phenomena. Taken in isolation, however, the interacting parts of a system fail to explain its dynamics behavior. A conceptual model is then translated into a mathematical model to explain system dynamics and interaction. Mathematical models often take the form of equations, logical rules or other mathematical mechanisms to represent the interrelations and relationships among the constituted parts of a system. Lastly, a simulation model is the computer-based implementation of mathematical models consisting of interrelated equations and logical rules. When a simulation model runs on a computer, it iteratively recalculates the modelled system state as it changes over time in accordance with the relationships represented by the mathematical relationships that describe the system dynamic. Therefore, developing detailed and dynamic simulation models comes at the cost of generality and interpretability, but it brings us realism and the ability to represent real-world processes in specific contexts. Simulation modelling is often used for prediction, exploration, theory development, or even optimization of conditions to achieve desired outcomes, with the goal of examining how the interconnections and relationships that characterise complex social and environmental systems (e.g. ecosystems, urban systems, social systems, global climate system) produces patterns of behavior over time. Therefore, simulation models are increasingly gaining relevance as scientific mechanisms for several reasons. First, simulation models allow researchers to study systems inaccessible to experimental and observational scientific methods, complementing more conventional approaches to discover or formalize theories about real world systems. Also, aS many real-world systems are nonlinear, simulation modelling has turned into a necessary method to explore and understand better such systems. In addition, the availability of computational science methods and technology, together with a large amount of data available from different sources, have greatly driven the adoption of simulation models in a wide range of scientific disciplines.
includes former concept " Model development"
Understand the defining characteristics of simulation models, and their applicability
Explain the role and purpose of computer simulation methods in geocomputation
Explain the process simulation model development
Evaluate the tradeoffs between abstraction and representativeness in simulation model development
Completed (GI-N2K)