Statevary

Statevary is a term used in some technical disciplines to describe the deliberate variation of a model or system’s internal state variables during experiments or simulations in order to study how outcomes depend on conditions that are not external inputs. The concept is not governed by a single universal standard and its exact meaning can differ across fields such as computer science, systems engineering, operations research, and epidemiology.

In practice, statevary involves selecting a set of representative state configurations, running simulations for each configuration, and then analyzing the results to assess sensitivity and robustness. It is often used in conjunction with other experimental methods, such as parameter sweeps or Monte Carlo sampling, to separate the influence of state dynamics from the effects of input parameters. Common metrics include effect sizes, variance in outcomes, and stability measures.

Statevary can complement traditional sensitivity analysis by focusing on the consequences of changes to the system’s internal conditions, initial states, or transition rules, rather than only varying external inputs. It requires clear definitions of what constitutes a distinct state, careful control of experimental design, and sufficient computational resources to explore the state space.

Examples of statevary applications include testing software models for robustness to internal flag settings, exploring epidemiological models by varying initial conditions or state distributions, and evaluating control systems under different internal state configurations.

See also: sensitivity analysis, parameter sweep, design of experiments, state space, Monte Carlo methods.