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A method for studying and managing complex feedback systems, such as those found in business or ecosystems, characterized by interdependent variables and regulated by feedback loops.

A mathematical study of waiting lines, or queues, using models to predict queue lengths and waiting times, commonly applied in operations management and telecommunications.

A computational algorithm that relies on repeated random sampling to obtain numerical results, typically used to model complex systems and evaluate risk in various fields such as finance, engineering, and research.

A simulation model where the state variables change continuously over time, and the system's equations are typically solved using numerical methods; applicable in fields like climate modeling or flight simulation.

A simulation paradigm where the state of the system changes at discrete points in time, triggered by events; commonly used in operations research and manufacturing.

Simulation techniques incorporating randomness and probabilistic elements to model systems that are inherently uncertain, often through random variables and processes.

A methodology used to explore and evaluate the potential outcomes of different decisions by considering various alternative future events or scenarios, often used in strategic planning.

In simulation, this refers to the initial phase where transient behaviors may occur before the system reaches steady-state performance; simulation results are often collected after this period.

A discrete model consisting of a grid of cells, each in one of a finite number of states, which evolves through a number of discrete time steps according to a set of rules based on the states of neighboring cells.

Using simulation models to test the effects of changes in input variables on the output, which can help in exploring different hypotheses or policy decisions.

The process of interpreting the data produced by a simulation, including techniques such as statistical inference to estimate model performance and reliability.

A class of computational models for simulating actions and interactions of autonomous agents, with the aim of assessing their effects on the system as a whole.

A technique used to determine how different values of an independent variable will impact a particular dependent variable under a given set of assumptions; crucial in validating simulation models.

The process of selecting a probability distribution to model random input variables in a simulation; this is a key step in ensuring simulation accuracy and credibility.