Speed-Up of Stochastic Simulation of PCTMC Models by Statistical Model Reduction

Abstract

Abstract. We present a novel statistical model reduction method which can significantly boost the speed of stochastic simulation of a population continuous-time Markov chain (PCTMC) model. This is achieved by identifying and removing agent types and transitions from the simulation which have only minor impact on the evolution of population dynamics of target agent types specified by the modeller. The error induced on the target agent types can be measured by a normalized coupling coefficient, which is calculated by an error propagation method over a directed relation graph for the PCTMC, using a limited number of simulation runs of the full model. Those agent types and transitions with minor impact are safely removed without incurring a significant error on the simulation result. To demonstrate the approach, we show the usefulness of our statistical reduction method by applying it to 50 randomly generated PCTMC models corresponding to different city bike-sharing scenarios.

Accelerating simulation of Population Continuous Time Markov Chains via automatic model reduction

“…We present algorithms for automatically carrying out the model reduction according to a user-specified decoupling threshold and for validation of the model with respect to an error threshold caused by the reduction. This represents a substantial modification of our preliminary work on this topic, which was presented in [23]. The current work makes a significant improvement in the efficiency with which the number of transition firings is estimated.…”

“…The current work makes a significant improvement in the efficiency with which the number of transition firings is estimated. In [23] we eliminated the non-influential parts of the model entirely; now we retain border transitions so that the influence of the trivial part of the model is not completely lost, improving the faithfulness of the reduced model. Moreover a novel validation technique for estimating the accuracy of the reduced model is presented.…”

Specification and Analysis of Open-Ended Systems with CARMA

“…This can be regarded as an example of a more general class of attribute-based communication mechanisms. Both multicast and unicast communication are supported [17], but in both cases only agents who enable the appropriate reception action have the ability to receive the message. The scope of communication is thus adjusted according to the perception function.…”