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We provide a review of the state of the art on the design and implementation of random number generators (RNGs) for simulation, on both sequential and parallel computing environments. We focus on the need for multiple independent streams and substreams of random numbers, explain how they can be constructed and managed, review software libraries that offer them, and illustrate their usefulness via...
Parallel and distributed simulation is a field concerned with the execution of a simulation program on computing platforms containing multiple processors. This article focuses on the concurrent execution of discrete event simulation programs. The field has evolved and grown from its origins in the 1970's and 1980's and remains an active field of research to this day. An overview of parallel and distributed...
For large-scale urban system simulations the computing power of traditional workstations is not sufficient. The move to High Performance Computing clusters is a viable solution. Users of such simulations are domain experts with little knowledge in computer science and optimization of such simulations. The access to HPC resources is also not available. Vendors have not sufficiently addressed this....
One of the major trends in traffic simulations is to take into account microscopic aspects of traffic flows at the street level. Multi-agent models such as MATSim (multi-agent transport simulation) have been highlighted for recent years as a solution to address these complex and microscopic simulation requirements. They are viewed as an emergent and collective behavior of agents, (i.e., vehicles)...
This paper proposes a newevolutionary algorithm-based methodology for optimal crowdevacuation planning. In the proposed methodology, a heuristic-based evacuation scheme is firstly introduced. The key idea is to divide the region into a set of sub-regions and use a heuristic rule to dynamically recommend an exit to agents in each sub-region. Then, an evolutionary framework based on the Cartesian Genetic...
Crowd simulation is a well-studied topic, yet it usually focuses on visualization. In this paper, we study a special class of crowd simulation, where individual agents have diverse backgrounds, ad hoc objectives, and non-repeating visits. Such crowd simulation is particularly useful when modeling human agents movement in leisure settings such as visiting museums or theme parks. In these settings,...
We propose a newstochastic model of infectious disease propagation. This model tracks individual outcomes, but does so without needing to create connectivity graphs for all members of the population. This makes the model scalable to much larger populations than traditional agent-based models have been able to cope with, while preserving the impact of variability during the critical early stages of...
Agent-based simulation (ABS) continues to grow in popularity and in its fast-expanding application in various fields. Despite the increased interest, however, a common protocol or standard curriculum for development and analysis of ABS models hardly exists. As originally discrete-event simulation (DES) modelers, self-taught and still new to the world of ABS modeling, we have occasionally observed...
Agent-based models (ABMs) are ubiquitous in research and industry. Currently, simulating ABMs involves at least some imperative (step-by-step) computer instructions. An alternative approach is declarative programming, in which a set of requirements is described at a high level of abstraction. Here I present the a fully declarative methodology for the automated construction of simulations for ABMs...
Insider threat modeling focuses primarily on the individual and the prediction of an insider threat incident. The majority of these models are statistical that tend toward trend-projections using various regression models. The modeling presented in this paper engages an agent-based paradigm that is designed to explore how an agent interacts with other employees and the organization in an environment...
Computer simulations are commonly used to model emergencies and discover useful evacuation strategies. The top-down conceptual models typically used for such simulations do not account for differences in individual behavior and how they affect other individuals. To create a more realistic model, this study uses Agent-Based Modeling (ABM) to simulate the evacuation of an urban population in case of...
Many Monte Carlo computations involve computing quantities that can be expressed as g(EX), where g is nonlinear and smooth, and X is an easily simulatable random variable. The nonlinearity of g makes the conventional Monte Carlo estimator for such quantities biased. In this paper, we show how such quantities can be estimated without bias. However, our approach typically increases the variance. Thus,...
Traditional stochastic approximation (SA) schemes employ a single gradient or a fixed batch of noisy gradients in computing a new iterate. We consider SA schemes in which Nk samples are utilized at step k and the total simulation budget is M, where equation and K denotes the terminal step. This paper makes the following contributions in the strongly convex regime: (I) We conduct an error analysis...
Performance analysis via stochastic simulation is often subject to input model uncertainty, meaning that the input model is unknown and needs to be inferred from data. Motivated especially from situations with limited data, we consider a worst-case analysis to handle input uncertainty by representing the partially available input information as constraints and solving a worst-case optimization problem...
When we use simulations to estimate the performance of a stochastic system, simulations are often driven by input distributions that are estimated from real-world data. There is both input and simulation uncertainty in the performance estimates. Non-parametric sampling approaches, e.g., the bootstrap, could be used to generate samples of input distributions quantifying both input model and parameter...
This work extends current research in model analysis and program understanding to assist modelers in obtaining additional insight into their models and the systems they represent. Given a particular simulation implementation, this research demonstrates the feasibility of automatically-derived observations that could potentially enhance a model builder or model user's understanding of their models...
Recent advancements in simulation and computing make it possible to compute large simulation ensembles. A simulation ensemble consists of multiple simulation runs of the same model with different values of control parameters. In order to cope with ensemble data, a modern analysis methodology is necessary. In this paper, we present our experience with simulation ensemble exploration and steering by...
Static network unreliability computation is an NP-hard problem, leading to the use of Monte Carlo techniques to estimate it. The latter, in turn, suffer from the rare event problem, in the frequent situation where the system's unreliability is a very small value. As a consequence, specific rare event event simulation techniques are relevant tools to provide this estimation. We focus here on a method...
Consider a system that is subjected to a random load and having a corresponding random capacity to withstand the load. The system fails when the load exceeds capacity, and we consider efficient simulation methods for estimating the failure probability. Our approaches employ various combinations of stratified sampling, Latin hypercube sampling, and conditional Monte Carlo. We construct asymptotically...
In this article we consider the efficient estimation of the tail distribution of the maximum of correlated normal random variables. We show that the currently recommended Monte Carlo estimator has difficulties in quantifying its precision, because its sample variance estimator is an inefficient estimator of the true variance. We propose a simple remedy: to still use this estimator, but to rely on...
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