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Output comparison between simulation model and real world reference system is commonly regarded to be the acid test of model credibility. As sound as the comparison-based approach may seem, serious epistemological and methodological qualifications have been made concerning the foundations of the concept, its applicability, and its dependence from the chosen philosophical perspective. The article reviews...
Agent_Zero is a formal alternative to the rational actor model that has dominated social science since the 1940s. This software individual is the first to be endowed with distinct affective, deliberative, and social modules. Grounded in neuroscience, these internal facets interact to produce far-from-rational individual behavior. And when ensembles of these agents interact spatially they generate...
Optimistic simulation yields impressive performance gains for many models. State saving is a quick way to provide the rollback mechanism required for this approach, but it has some drawbacks: it may not handle models with massive states or be able to support memory-constrained systems. This work presents a novel approach to state saving by storing only the relative changes caused by an event. Compressing...
Recent advancements in simulation and computing make it possible to compute large simulation ensembles. A simulation ensemble consists of multiple simulation runs of the same model with different values of control parameters. In order to cope with ensemble data, a modern analysis methodology is necessary. In this paper, we present our experience with simulation ensemble exploration and steering by...
We discuss copulas for incorporating dependence in the input distributions to a simulation model. We start by motivating the need for incorporating dependence in the primitive inputs to a simulation. Copulas are then introduced as a convenient and flexible model to incorporate dependence. We rigorously define copulas, introduce some of their basic properties, illustrate popular copula families, and...
In this tutorial we give an introduction to simulation optimization, covering its general form, central issues and common problems, basic methods, and a case study. Our target audience is users with experience in using simulation, but not necessarily experience with optimization. We offer guiding principles, and point to surveys and other tutorials that provide further information.
In the simulation-on-demand paradigm, we invest computational effort by running a simulation experiment before a question is asked, and then we quickly provide an answer by making use of the results of the earlier simulation experiment. This can be done by building a metamodel, but standard metamodeling methods used in stochastic simulation have the disadvantage that they require validation. We show...
The analysis of the behavior of simulation models and the subsequent communication of their results are critical but often neglected activities in simulation modeling. To overcome this issue, this paper proposes an integrated metamodeling approach based on structural equation modeling using the partial least squares algorithm. The suggested method integrates both a priori information from the conceptual...
Simulation is often used to study stochastic systems. A key step of this approach is to specify a distribution for the random input. This is called input modeling, which is important and even critical for simulation study. However, specifying a distribution precisely is usually difficult and even impossible in practice. This issue is called input uncertainty in simulation study. In this paper we study...
Reliable simulation estimation builds on accurately specified input models. In the context of simulating tail events, knowledge on the tail of the input model is especially important, yet is often hard to obtain due to a lack of data. In this paper, we consider tail event estimation without any knowledge on the input tail, but rather only making a general assumption that it is convex. We focus on...
We propose an efficient statistical method for the empirical model comparison, which is typically referred to as a simulation procedure to evaluate multiple statistical learning algorithms. First, we use experimental designs to appropriately construct the training and test sets for estimating the empirical performances of these models using the mean square errors. Second, we apply the idea of Bayesian...
International Community can decide to establish several types of sanctions on the economy of a country. No matter the reasons of these sanctions, generally, they can have many adverse effects on the economy of the sanctioned country. This depends on the variety and extent of the sanctions. We propose a system dynamics model to capture some of the main reasons behind the economic instability in a sanctioned...
Computer simulation has evolved to a standard means for planning, analyzing, and optimizing complex systems. Yet, a twofold usage can be observed: as a tool for generating data and as a method for deriving knowledge. The objective of this paper is to outline epistemological consequences arising from this methodological uncertainty by analyzing the state of discussion as well as challenges of using...
Optimal control of building's HVAC (Heating Ventilation and Air Conditioning) system as a demand response may not only reduce energy cost in buildings, but also reduce energy production in grid, stabilize energy grid and promote smart grid. In this paper, we describe a model predictive control (MPC) framework that optimally determines control profiles of the HVAC system as demand response. A Nonlinear...
We describe basic research that uses a causal, uncertainty-sensitive computational model rooted in qualitative social science to fuse disparate pieces of threat information. It is a cognitive model going beyond rational-actor methods. Having such a model has proven useful when information is uncertain, fragmentary, indirect, soft, conflicting, and even deceptive. Inferences from fusion must then account...
We combine in this paper an existing Agent-Based Model (ABM) of transitions in ship propulsion technologies with an expectations formulation from System Dynamics (SD). The reason for doing this was to take the best of two worlds, meaning that it may be able to implement another set of decision rules using SD than it is possible by solely employing ABM. In the ship model diffusion pathways are determined...
Optimization of discrete event systems conventionally uses simulation as a black-box oracle to estimate performance at design points generated by a separate optimization algorithm. This decoupled approach fails to exploit an important advantage: simulation codes are white-boxes, at least to their creators. In fact, the full integration of the simulation model and the optimization algorithm is possible...
A nosocomial infection is caused by microorganisms acquired in healthcare environments and it causes serious health problems to patients. Controlling the propagation of this infection is a topic of great interest in the health field. Our work focuses on propagation of nosocomial infection in emergency departments from the point of view of the physical contact among the people involved in the care...
In the analysis of input and output models used in computer simulation, parametric bootstrapping provides an attractive alternative to asymptotic theory for constructing confidence intervals for unknown parameter values and functions involving such parameter values, and also for calculating critical values of EDF statistics used in goodness-of-fit tests, such as the Anderson-Darling A2 statistic....
One of the major trends in traffic simulations is to take into account microscopic aspects of traffic flows at the street level. Multi-agent models such as MATSim (multi-agent transport simulation) have been highlighted for recent years as a solution to address these complex and microscopic simulation requirements. They are viewed as an emergent and collective behavior of agents, (i.e., vehicles)...
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