The design, visualization, manipulation, and implementation of models for computer simulation are key parts of the discipline. Models are constructed as a means to understand physical phenomena as state changes occur over time. One issue that arises is the need to correlate models and their components with the phenomena being modeled. For example, a part of an automotive engine needs to be placed into cognitive context with the diagrammatic icon that represents that part's function. A typical solution to this problem is to display a dynamic model of the engine in one window and the engine's CAD model in another. Users are expected to, on their own, mentally blend the dynamic model and the physical phenomenon into the same context. However, this contextualization is not trivial in many applications. Our approach expands upon this form of user interaction by specifying two ways in which dynamic models and the corresponding physical phenomena may be viewed, and experimented with, within the same human interaction space. We present a methodology and implementation of contextualization for diagram-based dynamic models using an anesthesia machine, and then follow up with a human study of its effects on spatial cognition.

An integrated Belief-Desire-Intention (BDI) modeling framework is proposed for human decision making and planning for evacuation scenarios, whose submodules are based on a Bayesian Belief Network (BBN), Decision-Field-Theory (DFT), and a Probabilistic Depth-First Search (PDFS) technique. A key novelty of the proposed model is its ability to represent both the human decision-making and decision-planning functions in a unified framework. To mimic realistic human behaviors, attributes of the BDI framework are reverse-engineered from human-in-the-loop experiments conducted in the Cave Automatic Virtual Environment (CAVE). The proposed modeling framework is demonstrated for a human's evacuation behaviors in response to a terrorist bomb attack. The simulated environment and agents (models of humans) conforming to the proposed BDI framework are implemented in AnyLogic® agent-based simulation software, where each agent calls external Netica BBN software to perform its perceptual processing function and Soar software to perform its real-time planning and decision-execution functions. The constructed simulation has been used to test the impact of several factors (e.g., demographics, number of police officers, information sharing via speakers) on evacuation performance (e.g., average evacuation time, percentage of casualties).

The field of game theory has proved to be of great importance in modeling interactions between self-interested parties in a variety of settings. Traditionally, game-theoretic analysis relied on highly stylized models to provide interesting insights about problems at hand. The shortcoming of such models is that they often do not capture vital detail. On the other hand, many real strategic settings, such as sponsored search auctions and supply-chains, can be modeled in high resolution using simulations. Recently, a number of approaches have been introduced to perform analysis of game-theoretic scenarios via simulation-based models. The first contribution of this work is the asymptotic analysis of Nash equilibria obtained from simulation-based models. The second contribution is to derive expressions for probabilistic bounds on the quality of Nash equilibrium solutions obtained using simulation data. In this vein, we derive very general distribution-free bounds, as well as bounds which rely on the standard normality assumptions, and extend the bounds to infinite games via Lipschitz continuity. Finally, we introduce a new maximum-a-posteriori estimator of Nash equilibria based on game-theoretic simulation data and show that it is consistent and almost surely unique.

When planning simulations of large-scale systems, it is important to anticipate what information is required to model the system and obtain desired output. This can be done without tying the study to a specific simulation package or language. It is valuable to do so to avoid unnecessarily long development and execution times. In this article, we offer a simulation information model (SIM) designed to help organize system information in the early stages of a project. (It can also be used to analyze existing models.) The SIM allows complexity analysis of the system to be performed, and may lead to a better selection of simulation language. The SIM is illustrated using two examples, and its relationship to current formalisms is discussed.

A setwise Gibbs sampler (SGS) method is developed to simulate stationary distributions and performance measures of network occupancy of Baskett-Chandy-Muntz-Palacios (BCMP) telecommunication models. It overcomes the simulation difficulty encountered in applying the standard Gibbs sampler to closed BCMP networks with constant occupancy constraints. We show Markov chains induced by SGS converge to the target stationary distributions. This article also investigates the filtered Gibbs sampler (FGS) as an efficient method for estimating various network performance measures. It shows that FGS's efficiency is considerable, but may be improperly overestimated. A more conservative performance estimator is then presented.

We propose to improve the efficiency of simulation optimization by integrating the notion of optimal computing budget allocation into the Cross-Entropy (CE) method, which is a global optimization search approach that iteratively updates a parameterized distribution from which candidate solutions are generated. This article focuses on continuous optimization problems. In the stochastic simulation setting where replications are expensive but noise in the objective function estimate could mislead the search process, the allocation of simulation replications can make a significant difference in the performance of such global optimization search algorithms. A new allocation scheme is developed based on the notion of optimal computing budget allocation. The proposed approach improves the updating of the sampling distribution by carrying out this computing budget allocation in an efficient manner, by minimizing the expected mean-squared error of the CE weight function. Numerical experiments indicate that the computational efficiency of the CE method can be substantially improved if the ideas of computing budget allocation are applied.

Analysts examining complex simulation models often conduct screening experiments to identify important factors. The controlled sequential bifurcation screening procedures CSB and CSB-X use a sequence of tests to classify factors as important or unimportant, while controlling Type I error and power. These procedures require analysts to identify the directions of the effects prior to experimentation, which can be problematic. We propose hybrid two-phase approaches, FFCSB and FFCSBX, as alternatives. Phase 1 uses an efficient fractional factorial to estimate factor effect directions; phase 2 uses CSB or CSB-X. Empirical investigations show these outperform CSB(X) in efficiency and effectiveness for many situations of practical interest.

This article reports on the application of a framework used to model, simulate and visualize the 3D structure of astrophysical wind volumes. The modeling methodology is similar to multidirectional medical tomography in that the spatial structure of an extended target can be reconstructed from a sequence of images obtained by scanning that target from several directions. Even though the controlled environment realized in diagnostic radiology cannot be replicated in the study of astrophysical phenomena, strong candidates for astrophysical tomography do exist in hot, close double stars locked in orbits around a common center of mass. As the Keplerian orbit is traced out, the geometry presented to the observer varies so that each star constitutes an analyzer upon its companion's wind and probes its structure. Since these targets are too far to be resolved spatially, we study and model the UV spectral lines of prominent wind ions, which scatter photospheric UV light so that line shapes vary as the stars revolve and as inhomogeneities form, propagate, and evolve in the composite wind. The framework presented is applied to two hot close binaries near the applicability limits of the discussed methodology. Two novel custom-made tools that aid the analysis of the spectra and the visualization of the results are also presented. The first of these, the Spectrum Analyzer and Animator , automates the derivation of light curves from the observed spectra and the generation of synthetic binary wind-line profiles, which reproduce the morphologies and variabilities of the observed wind profiles. After the composite wind structure of a binary has been recovered, the second tool, the Binary 3D Renderer —also authored in IDL—aids the visualization of the results by simulating the motion of the system (stars, winds and wind-interaction effects) around the binary's center of mass. The Binary 3D Renderer thus repackages the end product of a lengthy physical modeling process to generate physically sound, realistic multimedia content and increase the effectiveness and communication impact of the research results.

We present a simple, deterministic mathematical model for the spread of randomly scanning and bandwidth-saturating Internet worms. Such worms include Slammer and Witty, both of which spread extremely rapidly. Our model, consisting of coupled Kermack-McKendrick (a.k.a. stratified susceptibles-infectives (SI)) equations, captures both the measured scanning activity of the worm and the network limitation of its spread, that is, the effective scan-rate per worm/infective. The Internet is modeled as an ideal core network to which each peripheral (e.g., enterprise) network is connected via a single access link. It is further assumed in this note that as soon as a single end-system in the peripheral network is infected by the worm, the subsequent scanning of the rest of the Internet saturates the access link, that is, there is “instant” saturation. We fit our model to available data for the Slammer worm and demonstrate the model's ability to accurately represent Slammer's total scan-rate to the core.

This article discusses modeling and detection properties associated with the stochastic behavior of Random Constant Scanning (RCS) worms. Although these worms propagate by randomly scanning network addresses to find hosts that are susceptible to infection, traditional RCS worm models are fundamentally deterministic. A density-dependent Markov jump process model for RCS worms is presented and analyzed herein. Conditions are shown for when some stochastic properties of RCS worm propagation can be ignored and when deterministic RCS worm models can be used. A computationally simple hybrid deterministic/stochastic point-process model for locally observed scanning behavior due to the global propagation of an RCS scanning worm epidemic is presented. An optimal hypothesis-testing approach is presented to detect epidemics of these under idealized conditions based on the cumulative sums of log-likelihood ratios using the hybrid RCS worm model. This article presents in a mathematically rigorous fashion why detection techniques that are only based on passively monitoring local IP addresses cannot quickly detect the global propagation of an RCS worm epidemic with a low false alarm rate, even under idealized conditions.