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Experiences creating three implementations of the Repast agent modeling toolkit
 ACM Transactions on Modeling and Computer Simulation
, 2006
"... Many agentbased modeling and simulation researchers and practitioners have called for varying levels of simulation interoperability ranging from shared software architectures to common agent communications languages. These calls have been at least partially answered by several specifications and te ..."
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Cited by 129 (6 self)
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Many agentbased modeling and simulation researchers and practitioners have called for varying levels of simulation interoperability ranging from shared software architectures to common agent communications languages. These calls have been at least partially answered by several specifications and technologies. In fact, Tanenbaum [1988] has remarked that the “nice thing about standards is that there are so many to choose from. ” Tanenbaum goes on to say that “if you do not like any of them, you can just wait for next year’s model. ” This article does not seek to introduce next year’s model. Rather, the goal is to contribute to the larger simulation community the authors’ accumulated experiences from developing several implementations of an agentbased simulation toolkit. As such, this article focuses on the implementation of simulation architectures rather than agent communications languages. It is hoped that ongoing architecture standards efforts will benefit from this new knowledge and use it to produce architecture standards with increased robustness.
Statistical selection of the best system
, 2005
"... This tutorial discusses some statistical procedures for selecting the best of a number of competing systems. The term “best” may refer to that simulated system having, say, the largest expected value or the greatest likelihood of yielding a large observation. We describe various procedures for findi ..."
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Cited by 79 (7 self)
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This tutorial discusses some statistical procedures for selecting the best of a number of competing systems. The term “best” may refer to that simulated system having, say, the largest expected value or the greatest likelihood of yielding a large observation. We describe various procedures for finding the best, some of which assume that the underlying observations arise from competing normal distributions, and some of which are essentially nonparametric in nature. In each case, we comment on how to apply the above procedures for use in simulations.
A Taxonomy of Model Structures for Economic Evaluation of Health Technologies
 Health Economics
, 2006
"... Models for the economic evaluation of health technologies provide valuable information to decision makers. The choice of model structure is rarely discussed in published studies and can affect the results produced. Many papers describe good modelling practice, but few describe how to choose from the ..."
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Cited by 43 (4 self)
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Models for the economic evaluation of health technologies provide valuable information to decision makers. The choice of model structure is rarely discussed in published studies and can affect the results produced. Many papers describe good modelling practice, but few describe how to choose from the many types of available models. This paper develops a new taxonomy of model structures. The horizontal axis of the taxonomy describes assumptions about the role of expected values, randomness, the heterogeneity of entities, and the degree of nonMarkovian structure. Commonly used aggregate models, including decision trees and Markov models require large population numbers, homogeneous subgroups and linear interactions. Individual models are more flexible, but may require replications with different random numbers to estimate expected values. The vertical axis describes potential interactions between the individual actors, as well as how the interactions occur through time. Models using interactions, such as system dynamics, some Markov models, and discrete event simulation are fairly uncommon in the health economics but are necessary for modelling infectious diseases and systems with constrained
The KnowledgeGradient Policy for Correlated Normal Beliefs
"... We consider a Bayesian ranking and selection problem with independent normal rewards and a correlated multivariate normal belief on the mean values of these rewards. Because this formulation of the ranking and selection problem models dependence between alternatives’ mean values, algorithms may util ..."
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Cited by 41 (20 self)
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We consider a Bayesian ranking and selection problem with independent normal rewards and a correlated multivariate normal belief on the mean values of these rewards. Because this formulation of the ranking and selection problem models dependence between alternatives’ mean values, algorithms may utilize this dependence to perform efficiently even when the number of alternatives is very large. We propose a fully sequential sampling policy called the knowledgegradient policy, which is provably optimal in some special cases and has bounded suboptimality in all others. We then demonstrate how this policy may be applied to efficiently maximize a continuous function on a continuous domain while constrained to a fixed number of noisy measurements.
An adaptive sampling algorithm for solving Markov decision processes
 Operations Research
, 2005
"... Based on recent results for multiarmed bandit problems, we propose an adaptive sampling algorithm that approximates the optimal value of a finite horizon Markov decision process (MDP) with infinite state space but finite action space and bounded rewards. The algorithm adaptively chooses which actio ..."
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Cited by 37 (8 self)
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Based on recent results for multiarmed bandit problems, we propose an adaptive sampling algorithm that approximates the optimal value of a finite horizon Markov decision process (MDP) with infinite state space but finite action space and bounded rewards. The algorithm adaptively chooses which action to sample as the sampling process proceeds, and it is proven that the estimate produced by the algorithm is asymptotically unbiased and the worst possible bias is bounded by a quantity that converges to zero at rate O � � H ln N N,whereHis the horizon length and N is the total number of samples that are used per state sampled in each stage. The worstcase runningtime complexity of the algorithm is O((AN) H), independent of the state space size, where A  is the size of the action space. The algorithm can be used to create an approximate receding horizon control to solve infinite horizon MDPs.
StateoftheArt Review: A User’s Guide to the Brave New World of Designing Simulation Experiments
 INFORMS Journal on Computing
, 2005
"... informs ® doi 10.1287/ijoc.1050.0136 © 2005 INFORMS 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 expe ..."
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Cited by 23 (4 self)
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informs ® doi 10.1287/ijoc.1050.0136 © 2005 INFORMS 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 realworld 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 socalled 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 realworld 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
Grand Challenges in Modeling and Simulation of Complex Manufacturing Systems." Simulation 80.9 (2004
 Principles of Composite Material Mechanics
"... Even though we have moved beyond the Industrial Age and into the Information Age, manufacturing remains an important part of the global economy.There is a need for the pervasive use of modeling and simulation for decision support in current and future manufacturing systems, and several challenges ne ..."
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Cited by 20 (1 self)
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Even though we have moved beyond the Industrial Age and into the Information Age, manufacturing remains an important part of the global economy.There is a need for the pervasive use of modeling and simulation for decision support in current and future manufacturing systems, and several challenges need to be addressed by the simulation community to realize this vision. First, an order of magnitude reduction in problemsolving cycles is needed. The second grand challenge is the development of realtime, simulationbased problemsolving capability. The third grand challenge is the need for true plugandplay interoperability of simulations and supporting software. Finally, there is the biggest challenge facing modeling and simulation analysts today: that of convincing management to sponsor modeling and simulation projects instead of, or in addition to, more commonly used manufacturing system design and improvement methods such as lean manufacturing and six sigma.
Efficient jump ahead for F2linear random number generators
, 2006
"... The fastest longperiod random number generators currently available are based on linear recurrences modulo 2. So far, software that provides multiple disjoint streams and substreams has not been available for these generators because of the lack of efficient jumpahead facilities. In principle, i ..."
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Cited by 16 (2 self)
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The fastest longperiod random number generators currently available are based on linear recurrences modulo 2. So far, software that provides multiple disjoint streams and substreams has not been available for these generators because of the lack of efficient jumpahead facilities. In principle, it suffices to multiply the state (a kbit vector) by an appropriate k × k binary matrix to find the new state far ahead in the sequence. However, when k is large (e.g., for a generator such as the popular Mersenne twister, for which k = 19937), this matrixvector multiplication is slow and a large amount of memory is required to store the k × k matrix. In this paper, we provide a faster algorithm to jump ahead by a large number of steps in a linear recurrence modulo 2. The method uses much less than the k 2 bits of memory required by the matrix method. It is based on polynomial calculus modulo the characteristic polynomial of the recurrence and uses a sliding window algorithm for the multiplication. Key words: simulation; random number generation; jumping ahead; multiple streams 1.
Large deviation asymptotics and control variates for simulating large functions
, 2005
"... Consider the normalized partial sums of a realvalued function F of a Markov chain, φn: = n −1 n−1 F(Φ(k)), n ≥ 1. k=0 The chain {Φ(k) : k ≥ 0} takes values in a general state space X, with transition kernel P, and it is assumed that the Lyapunov drift condition holds: PV ≤ V −W +bIC where V: X → (0 ..."
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Cited by 16 (6 self)
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Consider the normalized partial sums of a realvalued function F of a Markov chain, φn: = n −1 n−1 F(Φ(k)), n ≥ 1. k=0 The chain {Φ(k) : k ≥ 0} takes values in a general state space X, with transition kernel P, and it is assumed that the Lyapunov drift condition holds: PV ≤ V −W +bIC where V: X → (0, ∞), W: X → [1, ∞), the set C is small, and W dominates F. Under these assumptions, the following conclusions are obtained: (i) It is known that this drift condition is equivalent to the existence of a unique invariant distribution π satisfying π(W) < ∞, and the Law of Large Numbers holds for any function F dominated by W: φn → φ: = π(F), a.s., n → ∞. (ii) The lower error probability defined by P{φn ≤ c}, for c < φ, n ≥ 1, satisfies a large deviation limit theorem when the function F satisfies a monotonicity condition. Under additional minor conditions an exact large deviations expansion is obtained. (iii) If W is nearmonotone then controlvariates are constructed based on the Lyapunov function V, providing a pair of estimators that together satisfy nontrivial large asymptotics for the lower and upper error probabilities. In an application to simulation of queues it is shown that exact large deviation asymptotics are possible even when the estimator does not satisfy a Central Limit Theorem.