WSC 2002 Final Abstracts

Simulation Optimization
Sigurdur Ólafsson and Jumi Kim (Iowa State University)

Abstract:
Simulation optimization has received considerable attention from both simulation researchers and practitioners. In this tutorial we present a broad introduction to simulation optimization and the many techniques that have been suggested to solve simulation optimization problems. Both continuous and discrete problems are discussed, but an emphasis is placed on discrete problems and practical methods for addressing such problems.

Output Data Analysis for Simulations
Christos Alexopoulos and Seong-Hee Kim (Georgia Institute of Technology)

Abstract:
This paper reviews statistical methods for analyzing output data from computer simulations. First, it focuses on the estimation of steady-state system parameters. The estimation techniques include the replication/deletion approach, the regenerative method, the batch means method, and the standardized time series method. Second, it reviews recent statistical procedures to find the best system among a set of competing alternatives.

Inside Discrete-Event Simulation Software: How it Works and Why it Matters
Thomas J. Schriber (University of Michigan) and Daniel T. Brunner (Systemflow Simulations, Inc.)

Abstract:
This paper provides simulation practitioners and consumers with a grounding in how discrete-event simulation software works. Topics include discrete-event systems; entities, resources, control elements and operations; simulation runs; entity states; entity lists; and entity-list management. The implementation of these generic ideas in AutoMod, SLX, and Extend is described. The paper concludes with several examples of "why it matters" for modelers to know how their simulation software works, including coverage of SIMAN (Arena), ProModel, and GPSS/H as well as the other three tools.

Adaptive Monte Carlo Methods for Rare Event Simulations
Ming-hua Hsieh (National Chengchi University)

Abstract:
We review two types of adaptive Monte Carlo methods for rare event simulations. These methods are based on importance sampling. The first approach selects importance sampling distributions by minimizing the variance of importance sampling estimator. The second approach selects importance sampling distributions by minimizing the cross entropy to the optimal importance sampling distribution. We also review the basic concepts of importance sampling in the rare event simulation context. To make the basic concepts concrete, we introduce these ideas via the study of rare events of M/M/1 queues.

Exploring the World of Agent-Based Simulations: Simple Models, Complex Analyses
Susan M. Sanchez and Thomas W. Lucas (Naval Postgraduate School)

Abstract:
Agent-based simulations are models where multiple entities sense and stochastically respond to conditions in their local environments, mimicking complex large-scale system behavior. We provide an overview of some important issues in the modeling and analysis of agent-based systems. Examples are drawn from a range of fields: biological modeling, sociological modeling, industrial applications, though we focus on recent results for a variety of military applications. Based on our experiences with various agent-based models, we describe issues that simulation analysts should be aware of when embarking on agent-based model development. We also describe a number of tools (both graphical and analytical) that we have found particularly useful for analyzing these types of simulation models. We conclude with a discussion of areas in need of further investigation.

Key Requirements for Cave Simulations
Scott M. Preddy and Richard E. Nance (Virginia Tech)

Abstract:
Virtual reality offers a new frontier for human interaction with simulation models. A virtual environment, such as that created with a CAVE, imposes either real-time or quasi-real-time performance on the simulation model. Beyond that general requirement, what others can be identified for simulation programs that drive a virtual reality or virtual environment interface? Based on experience with the Virginia Tech CAVE augmented by a literature search, we propose three key requirements for successful CAVE-based simulations: (1) Portability among CAVE-specific input/output devices, (2) effective and efficient inter-process communication, and (3) overcoming the limitations associated with input/output device interaction. Each requirement is described in some detail to both explain and justify its inclusion. Limitations and near- and intermediate-term research needs are identified.

Bayesian Statistics and the Monte Carlo Method
Thomas N. Herzog (U.S. Department of Housing and Urban Development)

Abstract:
We discuss the application of the Bayesian statistical paradigm in conjunction with Monte Carlo methods to practical problems. We begin by describing the basic constructs of the Bayesian paradigm. We then discuss two applications. The first entails the simulation of a two-stage model of a property-casualty insurance operation. The second application simulates the operation of an insurance regime for home equity conversion mortgages (also known as reverse mortgages). In this simulation, we built separate models to (1) predict the appreciation of individual home values and (2) predict the annual mortality experience of individual insureds. A feature of this work was the simulation of the parameters of these models in order to explicitly incorporate their variability into the model. We conclude the work by considering (1) model validation issues and (2) alternate forms of scenario testing – i.e., those employing pseudo-random numbers, quasi-random numbers, or even more subjective schemes.

Simulation-Based Engineering of Complex Systems Using EXTEND+MFG+OpEMCSS
John R. Clymer (California State University Fullerton)

Abstract:
A Complex Adaptive System (CAS) is a network of self-organizing, intelligent agents that share knowledge and adapt their operations in order to achieve overall system goals. Three things are needed to understand, design, and evaluate CAS. First, a mathematical model or way-of-thinking about CAS, called Context-Sensitive Systems (CSS) theory, is required to provide a solid foundation upon which to represent and describe the kinds of interactions that occur among the CAS agents during system operation. Second, a graphical modeling language is required that implements CSS theory in a way that enhances visualization and understanding of CAS. Third, a systems design and evaluation tool is required that makes it easy to apply CSS theory, expressed using a graphical modeling language, to understand, design, and evaluate CAS. As an example, an OpEMCSS model of two intelligent agents is discussed that learn rules and maximize their average reward in the prisoner’s dilemma game.

Human Performance Modeling for Discrete-Event Simulation: Workload
John Keller (Micro Analysis & Design, Inc.)

Abstract:
This tutorial will present a methodology for modeling of human performance using multiple resource theory within a discrete event simulation. Participants will gain an understanding of why modeling human performance can be important and how workload models can be used to support system design. This presentation will include the theoretical background as well as detailed the techniques for modeling workload. The techniques will be demonstrated through the development of a model to assess the workload associated with driving a car while talking on a cell phone. Finally, two case studies of how these techniques have been used to model human performance during the design of new military systems will be presented.