Carlosimuleringar

Carlosimuleringar is a term used in Swedish-language technical literature to describe a class of computational methods for modeling complex systems under uncertainty. Conceptually similar to Monte Carlo simulations, Carlosimuleringar use repeated random sampling to propagate uncertainty through a model and produce distributions for outcomes rather than single point estimates.

Typical workflow: define a model and its inputs with probability distributions, generate many random samples, run the model for each sample, and analyze the resulting output distribution. Variants include discrete-event simulations for processes with distinct events and times, stochastic differential equation models for continuous dynamics, and agent-based simulations that track heterogeneous entities.

Applications span engineering, finance, epidemiology, environmental science, and operations research. They are used to estimate risk and reliability, assess project timelines, forecast demand, or evaluate policy impacts under uncertainty. Strengths include the ability to handle uncertainty explicitly and to explore scenario ranges; limitations include computational cost, sensitivity to input distributions, potential for misinterpretation of probabilistic results, and the need for careful model validation.

History and naming: the term appears in Swedish texts as an informal or regional synonym for Monte Carlo–style simulations; it reflects the broad tradition of stochastic simulation methods that expanded with increasing computing power in the late 20th and early 21st centuries. Related concepts include Monte Carlo methods and stochastic simulation.