SPSC: a new execution policy for exploring discrete-time stochastic simulations

09/20/2019
by   Yu-Lin Huang, et al.
0

In this paper, we introduce a new method called SPSC (Simulation, Partitioning, Selection, Cloning) to estimate efficiently the probability of possible solutions in stochastic simulations. This method can be applied to any type of simulation, however it is particularly suitable for multi-agent-based simulations (MABS). Therefore, its performance is evaluated on a well-known MABS and compared to the classical approach, i.e., Monte Carlo.

READ FULL TEXT

page 1

page 2

page 3

page 4

research
04/01/2020

Discrete-time Simulation of Stochastic Volterra Equations

We study discrete-time simulation schemes for stochastic Volterra equati...
research
09/07/2018

Monte Carlo Tree Search with Scalable Simulation Periods for Continuously Running Tasks

Monte Carlo Tree Search (MCTS) is particularly adapted to domains where ...
research
03/17/2021

Direct simulation Monte Carlo for new regimes in aggregation-fragmentation kinetics

We revisit two basic Direct Simulation Monte Carlo Methods to model aggr...
research
07/24/2019

Multilevel Monte Carlo Simulations of Composite Structures with Uncertain Manufacturing Defects

By adopting a Multilevel Monte Carlo (MLMC) framework, we show that only...
research
04/04/2022

Monte Carlo Physarum Machine: Characteristics of Pattern Formation in Continuous Stochastic Transport Networks

We present Monte Carlo Physarum Machine: a computational model suitable ...
research
11/07/2018

CARAVAN: a framework for comprehensive simulation

We present a software framework called CARAVAN, which was developed for ...
research
03/07/2017

On time and consistency in multi-level agent-based simulations

The integration of multiple viewpoints became an increasingly popular ap...

Please sign up or login with your details

Forgot password? Click here to reset