Many simulation practitioners can get more from their analyses by using the statistical theory on design of experiments (DOE) developed specifically for exploring computer models. We discuss a toolkit of designs for simulators with limited DOE expertise who want to select a design and an appropriate analysis for their experiments. Furthermore, we provide a research agenda listing problems in the design of simulation experiments-as opposed to real-world experiments-that require more investigation. We consider three types of practical problems: (1) developing a basic understanding of a particular simulation model or system, (2) finding robust decisions or policies as opposed to so-called optimal solutions, and (3) comparing the merits of various decisions or policies. Our discussion emphasizes aspects that are typical for simulation, such as having many more factors than in real-world experiments, and the sequential nature of the data collection. Because the same problem type may be addressed through different design types, we discuss quality attributes of designs, such as the ease of design construction, the flexibility for analysis, and efficiency considerations. Moreover, the selection of the design type depends on the metamodel (response surface) that the analysts tentatively assume; for example, complicated metamodels require more simulation runs. We present several procedures to validate the metamodel estimated from a specific design, and we summarize a case study illustrating several of our major themes. We conclude with a discussion of areas that merit more work to achieve the potential benefits-either via new research or incorporation into standard simulation or statistical packages.

We present a concise representation of fractional factorials and an algorithm to quickly generate resolution V designs. The description is based on properties of a complete, orthogonal discretevalued basis set called Walsh functions. We tabulate two-level resolution V fractional factorial designs, as well as central composite designs allowing estimation of full second-order models, for experiments involving up to 120 factors. The simple algorithm provided can be used to characterize even larger designs, and a fast Walsh transform method quickly generates design matrices from our representation.

We extend the organizational ecology literature by examining the relationship between organization size and failure. Contrary to the typical monotonically declining relationship between organization size and failure rates found in ecology research, we show that this relationship varies by type of organization. Using data from censuses of Health Maintenance Organizations in the United States, we find that the relationship assumes an inverted U-shape for one type of HMO and a monotonically declining shape for another type of HMO. These relationships result from differences between the two types of HMOs in level of commitment to the organization and to the "liability of the middle."

There continues to be increasing interest from a broad range of disciplines in agent-based and artificial life simulations. This includes the Department of Defense-which uses simulations heavily in its decision making process. Indeed, military conflicts can have many attributes that are consistent with complex adaptive systems-such as many entities interacting with some degree of autonomy, each of which is continually making decisions to satisfy a variety of sometimes conflicting objectives. In this paper, we present three applications of agent-based simulations used to analyze military problems. The first uses the MANA model to explore the ability of the U.S. Army's networkbased Future Force to perform with degraded communications. The second studies how unmanned surface vehicles can be used in force protection missions with the Pythagoras model. The last example examines the standard Army squad size with an integrated effort using MANA, Pythagoras, and the high-resolution simulation JANUS.

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, and 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.