Communications of the ACM
Computational organization theory
Multiagent systems
Computational Modeling of Organizations Comes of Age
Computational & Mathematical Organization Theory
Very large fractional factorial and central composite designs
ACM Transactions on Modeling and Computer Simulation (TOMACS)
A two-phase screening procedure for simulation experiments
WSC '05 Proceedings of the 37th conference on Winter simulation
Controlled sequential factorial design for simulation factor screening
WSC '05 Proceedings of the 37th conference on Winter simulation
State-of-the-Art Review: A User's Guide to the Brave New World of Designing Simulation Experiments
INFORMS Journal on Computing
Computational & Mathematical Organization Theory
Proceedings of the 39th conference on Winter simulation: 40 years! The best is yet to come
Two-phase screening procedure for simulation experiments
ACM Transactions on Modeling and Computer Simulation (TOMACS)
INFORMS Journal on Computing
Computational & Mathematical Organization Theory
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Simulation experiments are typically faster, cheaper and more flexible than physical experiments. They are especially useful for pilot studies of complicated systems where little prior knowledge of the system behavior exists. One key characteristic of simulation experiments is the large number of factors and interactions between factors that impact decision makers. Traditional simulation approaches offer little help in analyzing large numbers of factors and interactions, which makes interpretation and application of results very difficult and often incorrect. In this paper we implement and demonstrate efficient design of experiments techniques to analyze large, complex simulation models. Looking specifically within the domain of organizational performance, we illustrate how our approach can be used to analyze even immense results spaces, driven by myriad factors with sometimes unknown interactions, and pursue optimal settings for different performance measures. This allows analysts to rapidly identify the most important, results-influencing factors within simulation models, employ an experimental design to fully explore the simulation space efficiently, and enhance system design through simulation. This dramatically increases the breadth and depth of insights available through analysis of simulation data, reduces the time required to analyze simulation-driven studies, and extends the state of the art in computational and mathematical organization theory.