146,738 research outputs found
Verification and validation of simulation models
Simulation Models;econometrics
A generic testing framework for agent-based simulation models
International audienceAgent-based modelling and simulation (ABMS) had an increasing attention during the last decade. However, the weak validation and verification of agent-based simulation models makes ABMS hard to trust. There is no comprehensive tool set for verification and validation of agent-based simulation models, which demonstrates that inaccuracies exist and/or reveals the existing errors in the model. Moreover, on the practical side, many ABMS frameworks are in use. In this sense, we designed and developed a generic testing framework for agent-based simulation models to conduct validation and verification of models. This paper presents our testing framework in detail and demonstrates its effectiveness by showing its applicability on a realistic agent-based simulation case study
Computer modeling and simulation: towards epistemic distinction between verification and validation
Verification and validation of computer codes and models used in simulation are two aspects of the scientific practice of high importance and have recently been discussed by philosophers of science. While verification is predominantly associated with the correctness of the way a model is represented by a computer code or algorithm, validation more often refers to model’s relation to the real world and its intended use. It has been argued that because complex simulations are generally not transparent to a practitioner, the Duhem problem can arise for verification and validation due to their entanglement; such an entanglement makes it impossible to distinguish whether a coding error or model’s general inadequacy to its target should be blamed in the case of the model failure. I argue that in order to disentangle verification and validation, a clear distinction between computer modeling (construction of mathematical computer models of elementary processes) and simulation (construction of models of composite objects and processes by means of numerical experimenting with them) needs to be made. Holding on to that distinction, I propose to relate verification (based on theoretical strategies such as inferences) to modeling and validation, which shares the common epistemology with experimentation, to simulation. To explain reasons of their intermittent entanglement I propose a weberian ideal-typical model of modeling and simulation as roles in practice. I suggest an approach to alleviate the Duhem problem for verification and validation generally applicable in practice and based on differences in epistemic strategies and scopes
Pedestrian Flow Simulation Validation and Verification Techniques
For the verification and validation of microscopic simulation models of
pedestrian flow, we have performed experiments for different kind of facilities
and sites where most conflicts and congestion happens e.g. corridors, narrow
passages, and crosswalks. The validity of the model should compare the
experimental conditions and simulation results with video recording carried out
in the same condition like in real life e.g. pedestrian flux and density
distributions. The strategy in this technique is to achieve a certain amount of
accuracy required in the simulation model. This method is good at detecting the
critical points in the pedestrians walking areas. For the calibration of
suitable models we use the results obtained from analyzing the video recordings
in Hajj 2009 and these results can be used to check the design sections of
pedestrian facilities and exits. As practical examples, we present the
simulation of pilgrim streams on the Jamarat bridge.
The objectives of this study are twofold: first, to show through verification
and validation that simulation tools can be used to reproduce realistic
scenarios, and second, gather data for accurate predictions for designers and
decision makers.Comment: 19 pages, 10 figure
Computer modeling and simulation: towards epistemic distinction between verification and validation
Verification and validation of computer codes and models used in simulation are two aspects of the scientific practice of high importance and have recently been discussed by philosophers of science. While verification is predominantly associated with the correctness of the way a model is represented by a computer code or algorithm, validation more often refers to model’s relation to the real world and its intended use. It has been argued that because complex simulations are generally not transparent to a practitioner, the Duhem problem can arise for verification and validation due to their entanglement; such an entanglement makes it impossible to distinguish whether a coding error or model’s general inadequacy to its target should be blamed in the case of the model failure. I argue that in order to disentangle verification and validation, a clear distinction between computer modeling (construction of mathematical computer models of elementary processes) and simulation (construction of models of composite objects and processes by means of numerical experimenting with them) needs to be made. Holding on to that distinction, I propose to relate verification (based on theoretical strategies such as inferences) to modeling and validation, which shares the common epistemology with experimentation, to simulation. To explain reasons of their intermittent entanglement I propose a weberian ideal-typical model of modeling and simulation as roles in practice. I suggest an approach to alleviate the Duhem problem for verification and validation generally applicable in practice and based on differences in epistemic strategies and scopes
A Petri Net Approach to Verify and Debug Simulation Models
Verification and Simulation share many issues, one is that simulation
models require validation and verification. In the context of simulation,
verification is understood as the task to ensure that an executable simulation model
matches its conceptual counterpart while validation is the task to ensure that a simulation
model represents the system under study well enough with respect to the goals of the simulation study.
Both, validation and verification, are treated in the
literature at a rather high level and seem to be more an art than
engineering. This paper considers discrete event simulation of
stochastic models that are formulated in a process-oriented language.
The ProC/B paradigm is used as a particular example of a class of
simulation languages which follow the common process interaction
approach and show common concepts used in performance modeling,
namely a) layered systems of virtual machines that contain resources and
provide services and b) concurrent processes that interact by
message passing and shared memory.
We describe how Petri net analysis
techniques help to verify and debug a large and detailed simulation model of
airport logistics. We automatically derive a Petri net that models the control
flow of a Proc/B model and we make use of invariant analysis and modelchecking to shed
light on the allocation of resources, constraints among entities and
causes for deadlocks
Cyber-Virtual Systems: Simulation, Validation & Visualization
We describe our ongoing work and view on simulation, validation and
visualization of cyber-physical systems in industrial automation during
development, operation and maintenance. System models may represent an existing
physical part - for example an existing robot installation - and a software
simulated part - for example a possible future extension. We call such systems
cyber-virtual systems.
In this paper, we present the existing VITELab infrastructure for
visualization tasks in industrial automation. The new methodology for
simulation and validation motivated in this paper integrates this
infrastructure. We are targeting scenarios, where industrial sites which may be
in remote locations are modeled and visualized from different sites anywhere in
the world.
Complementing the visualization work, here, we are also concentrating on
software modeling challenges related to cyber-virtual systems and simulation,
testing, validation and verification techniques for them. Software models of
industrial sites require behavioural models of the components of the industrial
sites such as models for tools, robots, workpieces and other machinery as well
as communication and sensor facilities. Furthermore, collaboration between
sites is an important goal of our work.Comment: Preprint, 9th International Conference on Evaluation of Novel
Approaches to Software Engineering (ENASE 2014
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