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Simulation

Agent-based model Organizational ABM: agent-directed simulation
The agent-directed simulation (ADS) metaphor distinguishes between two categories, namely "Systems for Agents" and "Agents for Systems." Systems for Agents (sometimes referred to as agents systems) are systems implementing agents for the use in engineering, human and social dynamics, military applications, and others

Complexity Complex simulations
In social science, the study on the emergence of macro-properties from the micro-properties, also known as macro-micro view in sociology. The topic is commonly recognized as social complexity that is often related to the use of computer simulation in social science, i.e.: computational sociology.

Deception - Simulation
Simulation consists of exhibiting false information. There are three simulation techniques: (copying another model), fabrication (making up a new model), and distraction (offering an alternative model)

Artificial intelligence - Cybernetics and brain simulation
In the 1940s and 1950s, a number of researchers explored the connection between neurology, information theory, and cybernetics

Complex adaptive system - Modeling and Simulation
CAS are occasionally modeled by means of agent-based models and complex network-based models. Agent-based models are developed by means of various methods and tools primarily by means of first identifying the different agents inside the model. Another method of developing models for CAS involves developing complex network models by means of using interaction data of various CAS components.

Complex adaptive system - Modeling and Simulation
Recently SpringerOpen/BioMed Central has launched an online open-access journal on the topic of Complex Adaptive Systems Modeling (CASM).

Neuroinformatics - Simulation of C. elegans roundworm neural system
Several software simulation models of the complete neural and muscular system, and to some extent the worm's physical environment, have been presented since 2004, and are in some cases available for downloading

Neuroinformatics - Simulation of Drosophila fruit fly neural system
The brain belonging to the fruit fly Drosophila is also thoroughly studied, and a simplified model simulated.

Neuroinformatics - Mouse brain mapping and simulation
Between 1995 and 2005, Henry Markram mapped the types of neurons and their connections in such a column.

The Blue Brain project, completed in December 2006, aimed at the simulation of a rat neocortical column, which can be considered the smallest functional unit of the neocortex (the part of the brain thought to be responsible for higher functions such as conscious thought), containing 10,000 neurons (and 108synapses). In November 2007, the project reported the end of the first phase, delivering a data-driven process for creating, validating, and researching the neocortical column.

An artificial neural network described as being "as big and as complex as half of a mouse brain" was run on an IBM blue gene supercomputer by a University of Nevada research team in A simulated time of one second took ten seconds of computer time. The researchers said they had seen "biologically consistent" nerve impulses flowed through the virtual cortex. However, the simulation lacked the structures seen in real mice brains, and they intend to improve the accuracy of the neuron model.

Hyperloop - Computer simulation
In September 2013, Ansys Corporation ran computational fluid dynamics simulations to model the aerodynamics of the capsule and shear stress forces that the capsule would be subjected to. The simulation showed that the capsule design would need to be significantly reshaped to avoid creating supersonic airflow, and that the gap between the tube wall and capsule would need to be larger. Sandeep Sovani said the simulation showed that Hyperloop has challenges but that he is convinced it is feasible.

Hyperloop - Computer simulation
In October 2013, the development team of the OpenMDAO software framework released an open-source model of the Hyperloop's propulsion system. Their model allowed for a preliminary conclusion that the concept is feasible, although the tube would need to be significantly larger than originally projected—on the order of 13 feet (4 m).

strongSwan - UML simulation environment
The focus of the strongSwan project lies on the strong Authentication by means of X.509-Certificates, as well as the optional safe storage of private key on smart cards with help of the standardized PKCS#11 interface, strongSwan certificate check lists and On-line Certificate Status Protocol (OCSP).

An important capability is the use of X.509 Certificate Attributes, which permits it to realize complex access control mechanisms on the basis of group memberships.

strongSwan is however simple to configure and works smoothly with nearly all other IPsec implementations, in particular also with various Microsoft Windows and Mac OS X-VPN-products.

strongSwan comes with a simulation environment based on User-mode Linux. A network of eight virtual hosts allows the user to enact a multitude of site-to-site and roadwarrior VPN scenarios.

Head-mounted display - Training and simulation
A key application for HMDs is training and simulation, allowing to virtually place a trainee in a situation that is either too expensive or too dangerous to replicate in real-life. Training with HMDs cover a wide range of applications from driving, welding and spray painting, flight and vehicle simulators, dismounted soldier training, medical procedure training and more.

Some critics of brain simulation believe that it is simpler to create general intelligent action directly without imitating nature. Some commentators have used the analogy that early attempts to construct flying machines modeled them after birds, but that modern aircraft do not look like birds. A computational argument is used in AI - What is this, where it is shown that, if we have a formal definition of general AI, the corresponding program can be found by enumerating all possible programs and then testing each of them to see whether it matches the definition. No appropriate definition currently exists. - Approaches to brain simulation Estimates of how much processing power is needed to emulate a human brain at verious levels (from Ray Kurzweil, and Anders Sandberg and Nick Bostrom), along with the fastest supercomputer from TOP500 mapped by year.

Some critics of brain simulation believe that it is simpler to create general intelligent action directly without imitating nature. Some commentators have used the analogy that early attempts to construct flying machines modeled them after birds, but that modern aircraft do not look like birds. A computational argument is used in AI - What is this, where it is shown that, if we have a formal definition of general AI, the corresponding program can be found by enumerating all possible programs and then testing each of them to see whether it matches the definition. No appropriate definition currently exists. - Approaches to brain simulation Although direct brain emulation using artificial neural networks on a high-performance computing engine is a common approach, there are other approaches. An alternative artificial brain implementation could be based on Holographic Neural Technology (HNeT) non linear phase coherence/decoherence principles. The analogy has been made to quantum processes through the core synaptic algorithm which has strong similarities to the QM wave equation.

Some critics of brain simulation believe that it is simpler to create general intelligent action directly without imitating nature. Some commentators have used the analogy that early attempts to construct flying machines modeled them after birds, but that modern aircraft do not look like birds. A computational argument is used in AI - What is this, where it is shown that, if we have a formal definition of general AI, the corresponding program can be found by enumerating all possible programs and then testing each of them to see whether it matches the definition. No appropriate definition currently exists. - Approaches to brain simulation EvBrain is a form of evolutionary software that can evolve "brainlike" neural networks, such as the network immediately behind the retina.

Some critics of brain simulation believe that it is simpler to create general intelligent action directly without imitating nature. Some commentators have used the analogy that early attempts to construct flying machines modeled them after birds, but that modern aircraft do not look like birds. A computational argument is used in AI - What is this, where it is shown that, if we have a formal definition of general AI, the corresponding program can be found by enumerating all possible programs and then testing each of them to see whether it matches the definition. No appropriate definition currently exists. - Approaches to brain simulation Since November 2008, IBM received a $4.9 million grant from the Pentagon for research into creating intelligent computers. The Blue Brain project is being conducted with the assistance of IBM in Lausanne. The project is based on the premise that it is possible to artificially link the neurons "in the computer" by placing thirty million synapses in their proper three-dimensional position.

Some critics of brain simulation believe that it is simpler to create general intelligent action directly without imitating nature. Some commentators have used the analogy that early attempts to construct flying machines modeled them after birds, but that modern aircraft do not look like birds. A computational argument is used in AI - What is this, where it is shown that, if we have a formal definition of general AI, the corresponding program can be found by enumerating all possible programs and then testing each of them to see whether it matches the definition. No appropriate definition currently exists. - Approaches to brain simulation In March 2008, Blue Brain project was progressing faster than expected: "Consciousness is just a massive amount of information being exchanged by trillions of brain cells." Some proponents of strong AI speculate that computers in connection with Blue Brain and Soul Catcher may exceed human intellectual capacity by around 2015, and that it is likely that we will be able to download the human brain at some time around 2050.

Some critics of brain simulation believe that it is simpler to create general intelligent action directly without imitating nature. Some commentators have used the analogy that early attempts to construct flying machines modeled them after birds, but that modern aircraft do not look like birds. A computational argument is used in AI - What is this, where it is shown that, if we have a formal definition of general AI, the corresponding program can be found by enumerating all possible programs and then testing each of them to see whether it matches the definition. No appropriate definition currently exists. - Approaches to brain simulation There are good reasons to believe that, regardless of implementation strategy, the predictions of realising artificial brains in the near future are optimistic

Software prototyping - Application definition or simulation software
As a solution specification technique, Application Simulation falls between low-risk, but limited, text or drawing-based mock-ups (or wireframes) sometimes called paper based prototyping, and time-consuming, high-risk code-based prototypes, allowing software professionals to validate requirements and design choices early on, before development begins

Software prototyping - Application definition or simulation software
To simulate applications one can also use software which simulate real-world software programs for computer based training, demonstration, and customer support, such as screencasting software as those areas are closely related. There are also more specialised tools.

Mind uploading - Simulation model scale
Since the function of the human mind, and how it might arise from the working of the brain's neural network, are poorly understood issues, mind uploading relies on the idea of neural network emulation

On the other hand, a molecule-scale simulation of the brain is not expected to be required, provided that the functioning of the neurons is not affected by quantum mechanical processes. The neural network emulation approach only requires that the functioning and interaction of neurons and synapses are understood. It is expected that it is sufficient with a black-box signal processing model of how the neurons respond to nerve impulses (electrical as well as chemical synaptic transmission).

A sufficiently complex and accurate model of the neurons is required

Since learning and long-term memory are believed to result from strengthening or weakening the synapses via a mechanism known as synaptic plasticity or synaptic adaptation, the model should include this mechanism. The response of sensory receptors to various stimuli must also be modeled.

Furthermore, the model may have to include metabolism, i.e. how the neurons are affected by hormones and other chemical substances that may cross the blood–brain barrier. It is considered likely that the model must include currently unknown neuromodulators, neurotransmitters and ion channels. It is considered unlikely that the simulation model has to include protein interaction, which would make it computationally complex.

The computational power and computer memory must however be sufficient to run such large simulations, preferably in real time.

Mind uploading - Simulation of Drosophila fruit fly neural system
The brain belonging to the fruit fly Drosophila is also thoroughly studied, and to some extent simulated. The Drosophila connectome, a complete list of the neurons and connections of the brain of Drosophila, is likely to be available in the near future.

Mind uploading - Rodent brain simulation
The initial goal of the project, completed in December 2006, was the simulation of a rat neocortical column, which can be considered the smallest functional unit of the neocortex (the part of the brain thought to be responsible for higher functions such as conscious thought), containing 10,000 neurons (and 108 synapses)

Mind uploading - Rodent brain simulation
An organization called the Brain Preservation Foundation was founded in 2010 and is offering a Brain Preservation Technology prize to promote exploration of brain preservation technology in service of humanity

Rydberg atom - Classical simulation
A simple 1/r potential results in a closed Keplerian elliptical orbit

With the application of a static electric field, the electron feels a continuously changing torque. The resulting trajectory becomes progressively more distorted over time, eventually going through the full range of angular momentum from L = LMAX, to a straight line L=0, to the initial orbit in the opposite sense L = -LMAX.

The time period of the oscillation in angular momentum (the time to complete the trajectory in figure 7), almost exactly matches the quantum mechanically predicted period for the wavefunction to return to its initial state, demonstrating the classical nature of the Rydberg atom.

Latency (engineering) - Latency in simulators and simulation
In simulation applications, 'latency' refers to the time delay, normally measured in milliseconds (1/1,000 sec), between initial input and an output clearly discernible to the simulator trainee or simulator subject. Latency is sometimes also called transport delay.

Some authorities distinguish between latency and transport delay by using the term 'latency' in the sense of the extra time delay of a system over and above the reaction time of the vehicle being simulated, but this requires a detailed knowledge of the vehicle dynamics and can be controversial.

Importance of Motion and Visual Latencies

Computer simulation Computer simulation

Computer simulation Simulation of a system is represented as the running of the system's model

Computer simulation Over 10 years ago, a desert-battle simulation of one force invading another involved the modeling of 66,239 tanks, trucks and other vehicles on simulated terrain around Kuwait, using multiple supercomputers in the DoD High Performance Computer Modernization Program Other examples include a 1-billion-atom model of material deformation; a 2.64-million-atom model of the complex maker of protein in all organisms, a ribosome, in 2005; a complete simulation of the life cycle of Mycoplasma genitalium in 2012; and the Blue Brain project at EPFL (Switzerland), begun in May 2005 to create the first computer simulation of the entire human brain, right down to the molecular level.

Computer simulation Because of the computational cost of simulation, computer experiments are used to perform inference such as uncertainty quantification.

Computer simulation - Simulation versus model
A computer model refers to the algorithms and equations used to capture the behavior of the system being modeled. By contrast, a computer simulation refers to the actual running of the program that contains these equations or algorithms. Simulation, therefore, refers to the result of running a model. In other words, you would not "build a simulation". You would "build a model", and then either "run a model" or "run a simulation".

Computer simulation - History
There are many types of computer simulations; their common feature is the attempt to generate a sample of representative scenarios for a model in which a complete enumeration of all possible states of the model would be prohibitive or impossible.

Computer simulation - Data preparation
The external data requirements of simulations and models vary widely. For some, the input might be just a few numbers (for example, simulation of a waveform of AC electricity on a wire), while others might require terabytes of information (such as weather and climate models).

Input sources also vary widely:

Control surfaces used to direct the progress of the simulation in some way;

Current or historical data entered by hand;

Values output for the purpose by other simulations, models, or processes.

"invariant" data is often built into the model code, either because the value is truly invariant (e.g., the value of π) or because the designers consider the value to be invariant for all cases of interest;

data can be entered into the simulation when it starts up, for example by reading one or more files, or by reading data from a preprocessor;

data can be provided during the simulation run, for example by a sensor network.

Because of this variety, and because diverse simulation systems have many common elements, there are a large number of specialized simulation languages. The best-known may be Simula (sometimes called Simula-67, after the year 1967 when it was proposed). There are now many others.

Because digital computer mathematics is not perfect, rounding and truncation errors multiply this error, so it is useful to perform an "error analysis" to confirm that values output by the simulation will still be usefully accurate.

Even small errors in the original data can accumulate into substantial error later in the simulation. While all computer analysis is subject to the "GIGO" (garbage in, garbage out) restriction, this is especially true of digital simulation. Indeed, observation of this inherent, cumulative error in digital systems was the origin of chaos theory.

Computer simulation - Types
Computer models can be classified according to several independent pairs of attributes, including:

Stochastic or deterministic (and as a special case of deterministic, chaotic) – see external links below for examples of stochastic vs. deterministic simulations

Continuous or discrete (and as an important special case of discrete, discrete event or DE models)

Local or distributed.

Another way of categorizing models is to look at the underlying data structures. For time-stepped simulations, there are two main classes:

Simulations which store their data in regular grids and require only next-neighbor access are called stencil codes. Many CFD applications belong to this category.

If the underlying graph is not a regular grid, the model may belong to the meshfree method class.

Equations define the relationships between elements of the modeled system and attempt to find a state in which the system is in equilibrium. Such models are often used in simulating physical systems, as a simpler modeling case before dynamic simulation is attempted.

Dynamic simulations model changes in a system in response to (usually changing) input signals.

Stochastic models use random number generators to model chance or random events;

It is often more important to be able to access the data produced by the simulation and to discover logic defects in the design or the sequence of events.

By the late 1980s, however, most "analog" simulations were run on conventional digital computers that emulate the behavior of an analog computer.

A special type of discrete simulation that does not rely on a model with an underlying equation, but can nonetheless be represented formally, is agent-based simulation. In agent-based simulation, the individual entities (such as molecules, cells, trees or consumers) in the model are represented directly (rather than by their density or concentration) and possess an internal state and set of behaviors or rules that determine how the agent's state is updated from one time-step to the next.

Distributed models run on a network of interconnected computers, possibly through the Internet. Simulations dispersed across multiple host computers like this are often referred to as "distributed simulations". There are several standards for distributed simulation, including Aggregate Level Simulation Protocol (ALSP), Distributed Interactive Simulation (DIS), the High Level Architecture (simulation) (HLA) and the Test and Training Enabling Architecture (TENA).

Computer simulation - CGI computer simulation
Formerly, the output data from a computer simulation was sometimes presented in a table or a matrix showing how data were affected by numerous changes in the simulation parameters

Similarly, CGI computer simulations of CAT scans can simulate how a tumor might shrink or change during an extended period of medical treatment, presenting the passage of time as a spinning view of the visible human head, as the tumor changes.

Other applications of CGI computer simulations are being developed to graphically display large amounts of data, in motion, as changes occur during a simulation run.

Computer simulation - Computer simulation in science
Generic examples of types of computer simulations in science, which are derived from an underlying mathematical description:

a numerical simulation of differential equations that cannot be solved analytically, theories that involve continuous systems such as phenomena in physical cosmology, fluid dynamics (e.g., climate models, roadway noise models, roadway air dispersion models), continuum mechanics and chemical kinetics fall into this category.

a stochastic simulation, typically used for discrete systems where events occur probabilistically and which cannot be described directly with differential equations (this is a discrete simulation in the above sense). Phenomena in this category include genetic drift, biochemical or gene regulatory networks with small numbers of molecules. (see also: Monte Carlo method).

Specific examples of computer simulations follow:

statistical simulations based upon an agglomeration of a large number of input profiles, such as the forecasting of equilibrium temperature of receiving waters, allowing the gamut of meteorological data to be input for a specific locale. This technique was developed for thermal pollution forecasting.

agent based simulation has been used effectively in ecology, where it is often called "individual based modeling" and is used in situations for which individual variability in the agents cannot be neglected, such as population dynamics of salmon and trout (most purely mathematical models assume all trout behave identically).

time stepped dynamic model. In hydrology there are several such hydrology transport models such as the SWMM and DSSAM Models developed by the U.S. Environmental Protection Agency for river water quality forecasting.

computer simulations have also been used to formally model theories of human cognition and performance, e.g., ACT-R

computer simulation using molecular modeling for drug discovery

computer simulation for studying the selective sensitivity of bonds by mechanochemistry during grinding of organic molecules.

Computational fluid dynamics simulations are used to simulate the behaviour of flowing air, water and other fluids. One-, two- and three-dimensional models are used. A one dimensional model might simulate the effects of water hammer in a pipe. A two-dimensional model might be used to simulate the drag forces on the cross-section of an aeroplane wing. A three-dimensional simulation might estimate the heating and cooling requirements of a large building.

An understanding of statistical thermodynamic molecular theory is fundamental to the appreciation of molecular solutions. Development of the Potential Distribution Theorem (PDT) allows this complex subject to be simplified to down-to-earth presentations of molecular theory.

Notable, and sometimes controversial, computer simulations used in science include: Donella Meadows' World3 used in the Limits to Growth, James Lovelock's Daisyworld and Thomas Ray's Tierra.

Computer simulation - Simulation environments for physics and engineering
Graphical environments to design simulations have been developed. Special care was taken to handle events (situations in which the simulation equations are not valid and have to be changed). The open project Open Source Physics was started to develop reusable libraries for simulations in Java, together with Easy Java Simulations, a complete graphical environment that generates code based on these libraries.

Computer simulation - Computer simulation in practical contexts
Computer simulations are used in a wide variety of practical contexts, such as:

analysis of air pollutant dispersion using atmospheric dispersion modeling

design of complex systems such as aircraft and also logistics systems.

Simulation of other computers is emulation.

behavior of structures (such as buildings and industrial parts) under stress and other conditions

design of industrial processes, such as chemical processing plants

strategic management and organizational studies

reservoir simulation for the petroleum engineering to model the subsurface reservoir

process engineering simulation tools.

robot simulators for the design of robots and robot control algorithms

urban simulation models that simulate dynamic patterns of urban development and responses to urban land use and transportation policies. See a more detailed article on Urban Environment Simulation.

traffic engineering to plan or redesign parts of the street network from single junctions over cities to a national highway network to transportation system planning, design and operations. See a more detailed article on Simulation in Transportation.

Here a human is part of the simulation and thus influences the outcome in a way that is hard, if not impossible, to reproduce exactly.

Vehicle manufacturers make use of computer simulation to test safety features in new designs. By building a copy of the car in a physics simulation environment, they can save the hundreds of thousands of dollars that would otherwise be required to build and test a unique prototype. Engineers can step through the simulation milliseconds at a time to determine the exact stresses being put upon each section of the prototype.

Furthermore, simulation results are often aggregated into static images using various ways of scientific visualization.

In debugging, simulating a program execution under test (rather than executing natively) can detect far more errors than the hardware itself can detect and, at the same time, log useful debugging information such as instruction trace, memory alterations and instruction counts. This technique can also detect buffer overflow and similar "hard to detect" errors as well as produce performance information and tuning data.

Computer simulation - Pitfalls
If, for instance, one of the key parameters (e.g., the net ratio of oil-bearing strata) is known to only one significant figure, then the result of the simulation might not be more precise than one significant figure, although it might (misleadingly) be presented as having four significant figures.

Computer simulation - Model calibration techniques
Unless these techniques are employed, the simulation model created will produce inaccurate results and not be a useful prediction tool.

For example, in traffic simulation, typical parameters include look-ahead distance, car-following sensitivity, discharge headway, and start-up lost time

Simulation models handle model inputs in different ways so traffic that enters the network, for example, may or may not reach its desired destination

The final step is to validate the model by comparing the results with what is expected based on historical data from the study area

Validating traffic simulation models requires comparing traffic estimated by the model to observed traffic on the roadway and transit systems

Simulation models are typically built using several different modeling theories that can produce conflicting results

Simulation models can be used as a tool to verify engineering theories, but they are only valid if calibrated properly

Computer simulation - Further reading
“A Resource Allocation Framework for Experiment-Based Validation of Numerical Models,” Journal of Mechanics of Advanced Materials and Structures (Taylor & Francis).

Computer simulation - Notes
A.K. Hartmann, Practical Guide to Computer Simulations, Singapore: World Scientific, 2009

S. Hartmann, The World as a Process: Simulations in the Natural and Social Sciences, in: R. Hegselmann et al. (eds.), Modelling and Simulation in the Social Sciences from the Philosophy of Science Point of View, Theory and Decision Library. Dordrecht: Kluwer 1996, 77–100.

E. Winsberg, Science in the Age of Computer Simulation. Chicago: University of Chicago Press, 2010.

P. Humphreys, Extending Ourselves: Computational Science, Empiricism, and Scientific Method. Oxford: Oxford University Press, 2004.

Discrete event simulation

In the field of simulation, a discrete-event simulation (DES), models the operation of a system as a discrete sequence of events in time. Each event occurs at a particular instant in time and marks a change of state in the system. Between consecutive events, no change in the system is assumed to occur; thus the simulation can directly jump in time from one event to the next.

This contrasts with continuous simulation in which the simulation continuously tracks the system dynamics over time. Instead of being event-based, this is called an activity-based simulation; time is broken up into small time slices and the system state is updated according to the set of activities happening in the time slice. Because discrete-event simulations do not have to simulate every time slice, they can typically run much faster than the corresponding continuous simulation.

Another alternative to event-based simulation is process-based simulation. In this approach, each activity in a system corresponds to a separate process, where a process is typically simulated by a thread in the simulation program. In this case, the discrete events, which are generated by threads, would cause other threads to sleep, wake, and update the system state.

The three-phase approach is used by a number of commercial simulation software packages, but from the user's point of view, the specifics of the underlying simulation method are generally hidden.

Discrete event simulation - Example
A common exercise in learning how to build discrete-event simulations is to model a queue, such as customers arriving at a bank to be served by a teller

Discrete event simulation - Components of a discrete-event simulation
In addition to the logic of what happens when system events occur, discrete event simulations include the following:

Discrete event simulation - State
A system state is a set of variables that captures the salient properties of the system to be studied. The state trajectory overtime S(t) can mathematically represented by a step function whose values change in correspondence of discrete events.

Discrete event simulation - Clock
The simulation must keep track of the current simulation time, in whatever measurement units are suitable for the system being modeled. In discrete-event simulations, as opposed to real-time simulations, time ‘hops’ because events are instantaneous – the clock skips to the next event start time as the simulation proceeds.

Discrete event simulation - Events list
The simulation maintains at least one list of simulation events. This is sometimes called the pending event set because it lists events that are pending as a result of previously simulated event but have yet to be simulated themselves. An event is described by the time at which it occurs and a type, indicating the code that will be used to simulate that event. It is common for the event code to be parametrized, in which case, the event description also contains parameters to the event code.

When events are instantaneous, activities that extend over time are modeled as sequences of events. Some simulation frameworks allow the time of an event to be specified as an interval, giving the start time and the end time of each event.

Single-threaded simulation engines based on instantaneous events have just one current event. In contrast, multi-threaded simulation engines and simulation engines supporting an interval-based event model may have multiple current events. In both cases, there are significant problems with synchronization between current events.

The pending event set is typically organized as a priority queue, sorted by event time. That is, regardless of the order in which events are added to the event set, they are removed in strictly chronological order. Several general-purpose priority queue algorithms have proven effective for discrete-event simulation, most notably, the splay tree. More recent alternatives include skip lists, calendar queues. and ladder queues.

Typically, events are scheduled dynamically as the simulation proceeds. For example, in the bank example noted above, the event CUSTOMER-ARRIVAL at time t would, if the CUSTOMER_QUEUE was empty and TELLER was idle, include the creation of the subsequent event CUSTOMER-DEPARTURE to occur at time t+s, where s is a number generated from the SERVICE-TIME distribution.

Discrete event simulation - Random-number generators
The simulation needs to generate random variables of various kinds, depending on the system model. This is accomplished by one or more Pseudorandom number generators. The use of pseudo-random numbers as opposed to true random numbers is a benefit should a simulation need a rerun with exactly the same behavior.

Discrete event simulation - Random-number generators
In gathering statistics from the running model, it is important to either disregard events that occur before the steady state is reached or to run the simulation for long enough that the bootstrapping behavior is overwhelmed by steady-state behavior

Discrete event simulation - Statistics
The simulation typically keeps track of the system's statistics, which quantify the aspects of interest. In the bank example, it is of interest to track the mean waiting times. In a simulation model, performance metrics are not analytically derived from probability distributions, but rather as averages over replications, that is different runs of the model. Confidence intervals are usually constructed to help asses the quality of the output.

Discrete event simulation - Ending condition
Because events are bootstrapped, theoretically a discrete-event simulation could run forever. So the simulation designer must decide when the simulation will end. Typical choices are “at time t” or “after processing n number of events” or, more generally, “when statistical measure X reaches the value x”.

Discrete event simulation - Simulation engine logic
The main loop of a discrete-event simulation is something like this:

Discrete event simulation - Start
Initialize Clock (usually starts at simulation time zero).

Discrete event simulation - Start
Schedule an initial event (i.e., put some initial event into the Events List).

Discrete event simulation - Diagnosing process issues
By accurately documenting the system inside a simulation model it is possible to gain a bird’s eye view of the entire system.

Discrete event simulation - Diagnosing process issues
A working model of a system allows management to understand performance drivers. A simulation can be built to include any number of performance indicators such as worker utilization, on-time delivery rate, scrap rate, cash cycles, and so on.

Discrete event simulation - Hospital applications
An operating theater is generally shared between several surgical disciplines

Discrete event simulation - Lab test performance improvement ideas
Many systems improvement ideas are built on sound principles, proven methodologies (Lean, Six Sigma, TQM, etc.) yet fail to improve the overall system. A simulation model allows the user to understand and test a performance improvement idea in the context of the overall system.

Discrete event simulation - Evaluating capital investment decisions
See also: Monte Carlo methods in finance; Corporate finance #Capital investment decisions and #Quantifying uncertainty.

Discrete event simulation - Evaluating capital investment decisions
Simulation modeling is commonly used to model potential investments. Through modeling investments decision-makers can make informed decisions and evaluate potential alternatives.

Discrete event simulation - Network simulators
Discrete event simulation is used in computer network to simulate new protocols for different network traffic scenarios before deployment.

Discrete event simulation - Discrete event simulation languages
General purpose simulation languages include: Arena/SIMAN, SimEvents, Promodel, GPSS, SLAM and MODSIM.

Discrete event simulation - Further reading
Myron H. MacDougall (1987). Simulating Computer Systems: Techniques and Tools. MIT Press.

William Delaney, Erminia Vaccari (1988). Dynamic Models and Discrete Event Simulation. Dekker INC.

Roger W. McHaney (1991). Computer Simulation: A Practical Perspective. Academic Press.

Michael Pidd (1998). Computer simulation in management science – fourth edition. Wiley.

A, Alan Pritsker, Jean J. O'Reilly (1999). Simulation with Visual SLAM and AweSim. Wiley.

Averill M. Law and W. David Kelton (2000). Simulation modeling and analysis – third edition. McGraw–Hill.

Bernard P. Zeigler, Herbert Praehofer and Tag Gon Kim (2000). Theory of modeling and simulation: Integrating discrete event and continuous complex dynamic systems – second edition. Academic Press.

Jerry Banks, John Carson, Barry Nelson and David Nicol (2005). Discrete-event system simulation – fourth edition. Pearson.

Computational fluid dynamics - Large eddy simulation

Volume rendering of a non-premixed swirl flame as simulated by LES.

Large eddy simulation (LES) is a technique in which the smallest scales of the flow are removed through a filtering operation, and their effect modeled using subgrid scale models. This allows the largest and most important scales of the turbulence to be resolved, while greatly reducing the computational cost incurred by the smallest scales. This method requires greater computational resources than RANS methods, but is far cheaper than DNS.

Computational fluid dynamics - Detached eddy simulation

Computational fluid dynamics - Detached eddy simulation
Detached eddy simulations (DES) is a modification of a RANS model in which the model switches to a subgrid scale formulation in regions fine enough for LES calculations

Computational fluid dynamics - Direct numerical simulation

Computational fluid dynamics - Direct numerical simulation
Direct numerical simulation (DNS) resolves the entire range of turbulent length scales. This marginalizes the effect of models, but is extremely expensive. The computational cost is proportional to . DNS is intractable for flows with complex geometries or flow configurations.

Computational fluid dynamics - Coherent vortex simulation
Goldstein and Oleg applied the FDV model to large eddy simulation, but did not assume that the wavelet filter completely eliminated all coherent motions from the subfilter scales

Modeling and simulation

For instance, if we wanted to design a racecar, but weren't sure what type of spoiler would improve traction the most, we would be able to use a computer simulation of the car to estimate the effect of different spoiler shapes on the coefficient of friction in a turn

More generally, M&S is using models, including emulators, prototypes, simulators, and stimulators, either statically or over time, to develop data as a basis for making managerial or technical decisions. The terms "modeling" and "simulation" are often used interchangeably.

The use of modeling and simulation (M&S) within engineering is well recognized. Simulation technology belongs to the tool set of engineers of all application domains and has been included in the body of knowledge of engineering management. M&S has already helped to reduce costs, increase the quality of products and systems, and document and archive lessons learned.

M&S is a discipline on its own. Its many application domains often lead to the assumption that M&S is pure application. This is not the case and needs to be recognized by engineering management experts who want to use M&S. To ensure that the results of simulation are applicable to the real world, the engineering manager must understand the assumptions, conceptualizations, and implementation constraints of this emerging field.

Modeling and simulation - Interest in simulations
Technically, simulation is well accepted. The 2006 National Science Foundation (NSF) Report on “Simulation-based Engineering Science” showed the potential of using simulation technology and methods to revolutionize the engineering science. Among the reasons for the steadily increasing interest in simulation applications are the following:

Using simulations is generally cheaper and safer than conducting experiments with a prototype of the final product. One of the biggest computers worldwide is currently designed in order to simulate the detonation of nuclear devices and their effects in order to support better preparedness in the event of a nuclear explosion. Similar efforts are conducted to simulate hurricanes and other natural catastrophes.

Simulations can often be even more realistic than traditional experiments, as they allow the free configuration of environment parameters found in the operational application field of the final product. Examples are supporting deep water operation of the US Navy or the simulating the surface of neighbored planets in preparation of NASA missions.

Simulations can often be conducted faster than real time. This allows using them for efficient if-then-else analyses of different alternatives, in particular when the necessary data to initialize the simulation can easily be obtained from operational data. This use of simulation adds decision support simulation systems to the tool box of traditional decision support systems.

Simulations allow setting up a coherent synthetic environment that allows for integration of simulated systems in the early analysis phase via mixed virtual systems with first prototypical components to a virtual test environment for the final system

Modeling and simulation - Modeling and Simulation as an Emerging Discipline
"The emerging discipline of M&S is based on developments in diverse computer science areas as well as influenced by developments in System Theories, Systems Engineering, Software Engineering, Artificial Intelligence, and more

Padilla et al. recommend in "Do we Need M&S Science" to distinguish between M&S Science, Engineering, and Applications.

Models can be composed of different units (models at finer granularity) linked to achieve a specific goal; for this reason they can be also called modelling solutions.

Modeling and simulation - Modeling and Simulation in Pharmacy Education
Addendum 1.3, Simulations for Introductory Pharmacy Practices Experiences – Approved June 2010, states:

Simulation may not be utilized to supplant or replace the minimum expectation for time spent in actual pharmacy practice settings as set forth in the previously established policy. Beyond the majority of time in actual pharmacy practice settings,colleges and schools may utilize simulation to account for no greater than 20%(e.g., 60 hours of a 300 hour IPPE program) of total IPPE time.

Some pharmacy colleges and schools host Virtual Reality and full environment simulation programs

Modeling and simulation - M&S Science contributes to the Theory of M&S, defining the academic foundations of the discipline. M&S Engineering is rooted in Theory but looks for applicable solution patterns. The focus is general methods that can be applied in various problem domains.

Modeling and simulation - M&S Science contributes to the Theory of M&S, defining the academic foundations of the discipline. M&S Applications solve real world problems by focusing on solutions using M&S. Often, the solution results from applying a method, but many solutions are very problem domain specific and are derived from problem domain expertise and not from any general M&S theory or method.

Modeling and simulation - Application Domains
There are many categorizations possible, but the following taxonomy has been very successfully used in the defense domain, and is currently applied to medical simulation and transportation simulation as well.

Analyses Support is conducted in support of planning and experimentation. Very often, the search for an optimal solution that shall be implemented is driving these efforts. What-if analyses of alternatives fall into this category as well.

Systems Engineering Support is applied for the procurement, development, and testing of systems. This support can start in early phases and include topics like executable system architectures, and it can support testing by providing a virtual environment in which tests are conducted.

Training and Education Support provides simulators, virtual training environments, and serious games to train and educate people.

A special use of Analyses Support is applied to ongoing business operations. Traditionally, decision support systems provide this functionality. Simulation systems improve their functionality by adding the dynamic element and allow to compute estimates and predictions, including optimization and what-if analyses.

Modeling and simulation - Individual Concepts
While modeling targets the conceptualization, simulation challenges mainly focus on implementation, in other words, modeling resides on the abstraction level, whereas simulation resides on the implementation level.

Modeling and simulation - Individual Concepts
Conceptualization and implementation – modeling and simulation – are two activities that are mutually dependent, but can nonetheless be conducted by separate individuals

Modeling and simulation - Academic Modeling and Simulation Programs
Modeling and Simulation has only recently become an academic discipline of its own. Formerly, those working in the field usually had a background in engineering.

The following institutions offer degrees in Modeling and Simulation:

University of Pennsylvania (Philadelphia, PA)

Old Dominion University (Norfolk, VA)

University of Alabama in Huntsville (Huntsville, AL)

University of Central Florida (Orlando, FL)

Embry Riddle Aeronautical University (Daytona beach, Florida)

University of New South Wales (Australia)

Center for Modeling and Simulation(M.Tech(Modelling & Simulation)) (University of Pune, India)

Columbus State University (Columbus, GA)

Modeling and simulation - Modeling and Simulation Body of Knowledge
The Modeling and Simulation Body of Knowledge (M&S BoK) is the domain of knowledge (information) and capability (competency) that identifies the modeling and simulation (M&S) community of practice and the M&S profession, industry, and market.

The M&S BoK Index is a set of pointers providing handles so that subject information content can be denoted, identified, accessed, and manipulated.

The development of M&S BoK Indices has been championed by SimSummit.

Modeling and simulation - Summary
In summary, three activities have to be conducted and orchestrated to ensure success: a model must be produced that captures formally the conceptualization, a simulation must implement this model, and management processes must ensure that model and simulation are interconnected and on the current state (which means that normally the model needs to be updated in case the simulation is changed as well).

Modeling and simulation - Summary
The military and defense domain, in particular within the United States, has been the main M&S champion, in form of funding as well as application of M&S

Computational science - Numerical simulations
Numerical simulations have different objectives depending on the nature of the task being simulated:

Reconstruct and understand known events (e.g., earthquake, tsunamis and other natural disasters).

Predict future or unobserved situations (e.g., weather, sub-atomic particle behaviour).

Massively multiplayer online game - Simulations
Some MMOGs have been designed to accurately simulate certain aspects of the real world. They tend to be very specific to industries or activities of very large risk and huge potential loss, such as rocket science, airplanes, trucks, battle tanks, submarines etc. Gradually as simulation technology is getting more mainstream, so too various simulators arrive into more mundane industries.

The initial goal of World War II Online was to create a map (in north western Europe) that had real world physics (gravity, air/water resistance, etc.), and ability for players to have some strategic abilities to its basic FPS/RPG role. While the current version is not quite a true simulated world, it is very complex and contains a large persistent world.

For example, flight simulation via an MMOG requires far less expenditure of time and money, is completely risk-free, and is far less restrictive (fewer regulations to adhere to, no medical exams to pass, and so on).

Telecoms senior executives who have taken the Equilibrium/Arbitrage simulation say it is the most intense, and most useful training they have ever experienced

Other online simulation games include War Thunder, Motor City Online, The Sims Online, and Jumpgate.

Wireless sensor network - Simulation of WSNs
Agent-based modelling was originally based on social simulation.

Wireless sensor network - Simulation of WSNs
Network simulators like OPNET, NetSim, NS2 and OMNeT can be used to simulate a wireless sensor network.

Functional design - Applied to 3D modeling and simulation
In this context, they mean a parametric model of an object where the parameters are tied to real-world design criteria, such as an axle that will adjust its diameter based on the strength of the material and the amount of force being applied to it in the simulation

Event chain methodology - Monte Carlo Simulations
Once events and event chains are defined, quantitative analysis using Monte Carlo simulation can be performed to quantify the cumulative effect of the events. Probabilities and effects of risks are used as input data for Monte Carlo simulation of the project schedule. In most real life projects, it is necessary to supplement the information regarding the uncertainties expressed as an event, with distributions related to duration, start time, cost, and other parameters.

Dynamical simulation Dynamical simulation, in computational physics, is the simulation of systems of objects that are free to move, usually in three dimensions according to Newton's laws of dynamics, or approximations thereto. Dynamical simulation is used in computer animation to assist animators to produce realistic motion, in industrial design (for example to simulate crashes as an early step in crash testing), and in video games. Body movement is calculated using time integration methods.

Dynamical simulation - Physics engines
The models used in Dynamical simulations determine how accurate these simulations are.

Dynamical simulation - Particle model
The first model which may be used in physics engines governs the motion of infinitesimal objects with finite mass called “particles.” This equation, called Newton’s Second law (see Newton's laws) or the definition of force, is the fundamental behavior governing all motion:

Dynamical simulation - Particle model
This equation will allow us to fully model the behavior of particles, but this is not sufficient for most simulations because it does not account for the rotational motion of rigid bodies. This is the simplest model that can be used in a physics engine and was used extensively in early video games.

Dynamical simulation - Inertial model
Bodies in the real world deform as forces are applied to them, so we call them “soft,” but often the deformation is negligibly small compared to the motion, and it is very complicated to model, so most physics engines ignore deformation. A body that is assumed to be non-deformable are called rigid body. Rigid body dynamics deals with the motion of objects that cannot change shape, size, or mass but can change orientation and position.

To account for rotational energy and momentum, we must describe how force is applied to the object using a moment, and account for the mass distribution of the object using an inertia tensor. We describe these complex interactions with an equation somewhat similar to the definition of force above:

where is the central inertia tensor, is the angular velocity vector, and is the moment of the jth external force about the mass center.

The inertia tensor describes the location of each particle of mass in a given object in relation to the object's center of mass. This allows us to determine how an object will rotate dependent on the forces applied to it. This angular motion is quantified by the angular velocity vector.

As long as we stay below relativistic speeds (see Relativistic dynamics), this model will accurately simulate all relevant behavior. This method requires the Physics engine to solve six ordinary differential equations at every instant we want to render, which is a simple task for modern computers.

Dynamical simulation - Euler model
Euler's equations

However, if we make a few intelligent changes to our system, simulation will become much easier, and our calculation time will decrease

The N terms are applied torques about the principal axes

The terms are angular velocities about the principal axes

The drawback to this model is that all the computation is on the front end, so it is still slower than we would like. The real usefulness is not apparent because it still relies on a system of non-linear differential equations. To alleviate this problem, we have to find a method that can remove the second term from the equation. This will allow us to integrate much more easily. The easiest way to do this is to assume a certain amount of symmetry.

Dynamical simulation - Symmetric/torque model
The two types of symmetric objects that will simplify Euler's equations are “symmetric tops” and “symmetric spheres.” The first assumes one degree of symmetry, this makes two of the I terms equal

Dynamical simulation - Symmetric/torque model
These equations allow us to simulate the behavior of an object that can spin in a way very close to the method simulate motion without spin. This is a simple model but it is accurate enough to produce realistic output in real-time Dynamical simulations. It also allows a Physics engine to focus on the changing forces and torques rather than varying inertia.

Nuclear magnetic resonance - Animations and Simulations
This animation shows a spin, the modification of spin with magnetic field and HF pulse, spin echo sequences, inversion recovery sequence, gradient echo sequence and relaxation of spin

Nuclear magnetic resonance - Animations and Simulations
A free interactive simulation of NMR principles

Quantum algorithm - Quantum simulation
Efficient (that is, polynomial-time) quantum algorithms have been developed for simulating both Bosonic and Fermionic systems and in particular, the simulation of chemical reactions beyond the capabilities of current classical supercomputers requires only a few hundred qubits

AI - Cybernetics and brain simulation
In the 1940s and 1950s, a number of researchers explored the connection between neurology, information theory, and cybernetics

Emulator - Emulation versus simulation
The word emulator was coined in 1963 at IBM during development of the NPL (IBM 360) product line, using a new combination of software, microcode, and hardware. pages

They discovered that using microcode hardware instead of software simulation, to execute programs written for earlier IBM computers, dramatically increased simulation speed. Earlier, IBM provided Interpreter (computing)|simulators for, e.g., the IBM 650|650 on the IBM 705|705.

In addition to simulators, IBM had compatibility features on the IBM 709|709 and IBM 7090|7090, for which it

provided the IBM 709 computer with a program to run legacy programs written for the IBM 704 on the IBM 709|709 and later on the IBM This program used the instructions

;ESNT:Enter Storage Nullification and Transfer

;LSNM:Leave Storage Nullification Mode

added by the compatibility feature to trap instructions requiring special handling; all other 704 instructions ran the same on a The compatibility feature on the IBM 1410|1410 only required setting a console toggle switch, not a support program.

In 1963, when microcode was first used to speed up this simulation process, IBM engineers coined the term emulator to describe the concept.

Computer simulation is used in virtually every scientific and engineering domain and Computer Science is no exception, with several projects simulating abstract models of computer systems, such as Network simulation.

Software application - Simulation software
* Computer simulators

** Computational science|Scientific simulators

** Military simulation|Battlefield simulators

** Emergency simulators

** Vehicle simulators

** Simulation games

*** Vehicle simulation games

Push email - Simulation using traditional email
Traditional mobile mail clients may poll for new mail at frequent intervals, with or without downloading the mail to the client, thus providing a similar user experience as push .

Push email - Simulation using traditional email
IMAP in fact allows many notifications to be sent at any time, but not message data. The IDLE command is often used to signal the ability of a client to process notifications sent outside of a command running, which effectively provides a user experience identical to push.

Computer-generated imagery - Interactive simulation and visualization
Interactive visualization is a general term that applies to the rendering of data that may vary dynamically and allowing a user to view the data from multiple perspectives

Computer-generated imagery - Interactive simulation and visualization
At the abstract level an interactive visualization process involves a 'data pipeline in which the raw data is managed and filtered to a form that makes it suitable for rendering

Hard problem of consciousness - Simulation argument
the 'external' physical world (see references in the corresponding Wikipedia articles: Simulation hypothesis and Simulated reality).

Methods of virtual reality - Simulation-based VR
The simulator normally consists of several systems as follows: a real-time vehicle simulation system performing real-time simulation of vehicle dynamics; motion, visual and audio systems reproducing vehicle motion, driving environment scenes and noise sensed by a driver during driving; a control force roading system acting as an interface between the driver and the simulator; an Operator (profession)|operator Virtual console|console for monitoring system operation; and system integration managing information and data transfer among subsystems and synchronization

Contiki - Simulation A single Cooja simulation may contain a mixture of nodes from either of the three classes

Contiki - Simulation In Contiki 2.6, platforms with the TI MSP430 and Atmel AVR microcontrollers can be emulated.

Unity (game engine) - Simulations using Unity
* Universe Sandbox

Electronic design automation - Simulation
* SPICE|Transistor simulation – low-level transistor-simulation of a schematic/layout's behavior, accurate at device-level.

* Logic simulation – digital-simulation of an Register-transfer level|RTL or gate-netlist's digital (boolean 0/1) behavior, accurate at boolean-level.

* 'Behavioral Simulation' – high-level simulation of a design's architectural operation, accurate at cycle-level or interface-level.

* Hardware emulation – Use of special purpose hardware to emulate the logic of a proposed design. Can sometimes be plugged into a system in place of a yet-to-be-built chip; this is called 'in-circuit emulation'.

* Technology CAD simulate and analyze the underlying process technology. Electrical properties of devices are derived directly from device physics.

* Electromagnetic field solvers, or just Electromagnetic field solver|field solvers, solve Maxwell's equations directly for cases of interest in IC and PCB design. They are known for being slower but more accurate than the layout extraction above.

Internet radio - Simulation
A local tuner simulation program includes all the online radios that can also be heard in the air in the city.

Simulated reality - Simulation argument
The simulation hypothesis was first published by Hans Moravec.Moravec, Hans, [ Simulation, Consciousness, Existence]Moravec, Hans,Platt, Charles [ Superhumanism]Moravec, Hans [ Pigs in Cyberspace] Later, the philosopher Nick Bostrom developed an expanded argument examining the probability of our reality being a simulacrum.[ Are You Living in a Computer Simulation?] by Nick Bostrom

:1. Human civilization is unlikely to reach a level of transhumanism|technological maturity capable of producing simulated realities, or such simulations are physically impossible to construct.

:2. A comparable civilization reaching aforementioned technological status will likely not produce a significant number of simulated realities (one that might push the probable existence of digital entities beyond the probable number of real entities in a Universe) for any of a number of reasons, such as, diversion of computational processing power for other tasks, ethical considerations of holding entities captive in simulated realities, etc.

:3. Any entities with our general set of experiences are almost certainly living in a simulation.

His argument rests on the premise that given sufficiently advanced technology, it is possible to represent the populated surface of the Earth without recourse to digital physics|quantum simulation; that the qualia experienced by a simulated consciousness is comparable or equivalent to that of a naturally occurring human consciousness; and that one or more levels of simulation within simulations would be feasible given only a modest expenditure of computational resources in the real world.

If one assumes that humans will not be destroyed or destroy themselves before developing such a technology; and if one assumes that human descendants will have no overriding legal restrictions or moral compunctions against simulating biospheres or their own historical biosphere; then it would be unreasonable to count ourselves among the small minority of genuine organisms who, sooner or later, will be vastly outnumbered by artificial simulations.

For example, Bostrom suggests that a window could popup saying: You are living in a simulation

Simulated reality - Nested simulations
The existence of simulated reality is unprovable in any concrete sense: any evidence that is directly observed could be another simulation itself. In other words, there is an infinite regress problem with the argument. Even if we are a simulated reality, there is no way to be sure the beings running the simulation are not themselves a simulation, and the operators of that simulation are not a simulation.

involves a simulation, or an entity in the simulation, creating another instance of the same simulation, running it and using its results (Pooch and Sullivan 2000).

The term Matryoshkaverse refers to any universe that is locked inside and also encompasses a vast number of other universes, like a set of Russian wooden dolls with one or more dolls each nested inside another (Solomon, 2013).Solomon, Mark. On Computer Simulated Universes. Hillsborough, NC: Lithp Preth Publishing, 2013, pp ISBN=

Simulation sickness 'Motion sickness' or 'kinetosis', also known as 'travel sickness', is a condition in which a disagreement exists between visually perceived movement and the vestibular system's sense of movement. Depending on the cause, it can also be referred to as seasickness, car sickness, simulation sickness or airsickness.

Simulation sickness Dizziness, Fatigue (medical)|fatigue, and nausea are the most common symptoms of motion sickness.[ Motion Sickness Prevention and Treatment] Sopite syndrome in which a person feels fatigue or tiredness is also associated with motion sickness

Simulation sickness - Cause
The most common hypothesis for the cause of motion sickness is that it functions as a defense mechanism against neurotoxins.[ Motion sickness: an evolutionary hypothesis] The area postrema in the human brain|brain is responsible for inducing vomiting when poisons are detected, and for resolving conflicts between vision and balance

Simulation sickness - Types
# Motion sickness caused by motion that is felt but not seen

Simulation sickness - Types
# Motion sickness caused when both systems detect motion but they do not correspond.

Simulation sickness - Motion is felt but not seen
In these cases, motion is sensed by the vestibular system and hence the motion is felt, but no motion or little motion is detected by the visual system.

Simulation sickness - Carsickness
A specific form of motion sickness, car sickness is quite common and often evidenced by the inability to read a map or book during travel

Simulation sickness - Airsickness
Airsickness is a sensation which is induced by air travel

Simulation sickness - Sea-sickness
Seasickness is a form of motion sickness characterized by a feeling of nausea and, in extreme cases, Vertigo (medical)|vertigo experienced after spending time on a craft on water

Simulation sickness - Centrifuges
Rotating devices such as Centrifuge#Aeronautics_and_astronautics|centrifuges used in astronaut training and amusement park rides such as the Rotor (ride)|Rotor, Mission: Space and the Gravitron can cause motion sickness in many people

Simulation sickness - Dizziness due to spinning
When one spins and stops suddenly, fluid in the inner ear continues to rotate causing a sense of continued spinning while one's visual system no longer detects motion.

Simulation sickness - Motion that is seen but not felt
In these cases, motion is detected by the visual system and hence the motion is seen, but no motion or little motion is sensed by the vestibular system. Motion sickness arising from such situations has been referred to as Visually Induced Motion Sickness (VIMS).So, R.H.Y. and Ujike, H. (2010) Visually induced motion sickness, visual stress and photosensitive epileptic seizures: what do they have in common? - Preface to the special issue. Applied Ergonomics, 41(4), pp

Simulation sickness - Motion sickness due to films and other video
This type of sickness is particularly prevalent when susceptible people are watching films on large screens such as IMAX but may also occur in regular format theaters or even when watching TV. For the sake of novelty, IMAX and other panoramic type theaters often show dramatic motions such as flying over a landscape or riding a roller coaster. This type of motion sickness can be prevented by closing one's eyes during such scenes.

In regular format theaters, an example of a movie that caused motion sickness in many people is The Blair Witch Project. Theaters warned patrons of its possible nauseating effects, cautioning pregnant women in particular. Blair Witch was filmed with a handheld camcorder, which was subjected to considerably more motion than the average movie camera.

Home movies, often filmed with a hand-held camera, also tend to cause motion sickness in those that view them

Simulation sickness Simulation sickness, or simulator sickness, is a condition where a person exhibits symptoms similar to motion sickness caused by playing computer/simulation/video games.

Simulation sickness The symptoms are often described as quite similar to that of motion sickness, and can range from headache, drowsiness, nausea, dizziness, vomiting and sweating. Research done at the University of Minnesota had students play Halo (series)|Halo for less than an hour, and found that up to 50 percent felt sick afterwards.[ Could video games be making your kids sick?]

In a study conducted by U.S
Simulation sickness In a study conducted by U.S

Simulation sickness The phenomenon was well known in popular culture before it was known as simulation sickness. In the 1983 comedy film Joysticks (film)|Joysticks, the manager of a local video arcade says, The reason why I never play any of these games, well, they make me physically ill. I mean, every time I look in one of the screens, they make me dizzy.

Simulation sickness - Motion sickness due to virtual reality
Motion sickness due to Virtual Reality is very similar to simulation sickness and motion sickness due to films

Simulation sickness - Space sickness
Space sickness was effectively unknown during the earliest spaceflights, as these were undertaken in very cramped conditions; it seems to be aggravated by being able to freely move around, and so is more common in larger spacecraft

Simulation sickness - Coriolis effect
When moving within a rotating reference frame such as in a centrifuge or environment where Artificial_gravity#Rotation|gravity is simulated with centrifugal force, the coriolis effect causes a sense of motion in the vestibular system that does not match the motion that is seen.

Simulation sickness - Coriolis effect
Sometimes when riding a vehicle for a long time on a badly maintained road at a very slow (10–20km/h) speed the two senses fail to correspond. Due to the poor road quality the vehicle will jerk too much giving a sense of severe motion to the inner ear, but due to the slow speed the eye doesn't sense a proportional amount of motion.

Simulation sickness - Treatment
Many cures and preventatives for motion sickness have been proposed.

Simulation sickness - Device
One eyewear device named ViBAN was issued a U.S

Simulation sickness - Activity
One common suggestion is to simply look out of the window of the moving vehicle and to gaze towards the horizon in the direction of travel. This helps to re-orient the inner Equilibrioception|sense of balance by providing a visual reaffirmation of motion.

In the night, or in a ship without windows, it is helpful to simply close one's eyes, or if possible, take a nap. This resolves the input conflict between the eyes and the inner ear. Napping also helps prevent psychogenic effects (i.e. the effect of sickness being magnified by thinking about it).

A simple method for relieving common and mild car sickness is chewing. Chewing gum has an uncanny effectiveness for reducing car sickness in those affected. Chewing gum, however, is not the only thing one may chew to relieve mild effects of car sickness, snacking on sweets or just chewing in general seems to reduce adverse effects of the conflict between vision and balance.

Fresh, cool air can also relieve motion sickness slightly, although it is likely this is related to avoiding foul odors which can worsen nausea.[ FAA Medical Certification / Alcohol / Substance / Drugs - Motion Sickness]

Simulation sickness - Medication
Over-the-counter and prescription medications are readily available, such as Dramamine (dimenhydrinate), Stugeron (cinnarizine), and Bonine/Antivert (meclizine)

Scopolamine is effective and is sometimes used in the form of transdermal patches (1.5mg) or as a newer tablet form (0.4mg). The selection of a transdermal patch or scopolamine tablet is determined by a doctor after consideration of the patient's age, weight, and length of treatment time required.

Interestingly, many pharmacological treatments which are effective for nausea and vomiting in some medical conditions may not be effective for motion sickness

Ginger root is commonly thought to be an effective anti-emetic

Ginger is reported to calm the pyloric valve located at the base of the stomach. This relaxation of the valve allows the stomach to operate normally whereby the contents will enter the small intestine instead of being retained within the stomach. It is this undesirable effect of retention in the stomach that eventually results in vomiting. Vomiting is not seasickness but is only a symptom or side effect; although the effect most commonly associated with seasickness.

This link reports on a ginger study; notice the comment about less vomiting when taking ginger, but not less nausea. Whether ginger ale is as effective as ginger root for this purpose has been disputed.

Simulation sickness - Electronic
As astronauts frequently have motion sickness, NASA has done extensive research on the causes and treatments for motion sickness. One very promising looking treatment is for the person suffering from motion sickness to wear LCD shutter glasses that create a stroboscopic vision of 4Hz with a dwell of 10 milliseconds.[ Stroboscopic Vision as a Treatment for Space Motion Sickness]

Simulation 'Simulation' is the imitation of the operation of a real-world process or system over time. The act of simulating something first requires that a model be developed; this model represents the key characteristics or behaviors/functions of the selected physical or abstract system or process. The model represents the system itself, whereas the simulation represents the operation of the system over time.

Simulation Simulation is also used when the real system cannot be engaged, because it may not be accessible, or it may be dangerous or unacceptable to engage, or it is being designed but not yet built, or it may simply not exist.

Simulation Key issues in simulation include acquisition of valid source information about the relevant selection of key characteristics and behaviours, the use of simplifying approximations and assumptions within the simulation, and fidelity and validity of the simulation outcomes.

Simulation - Classification and terminology
Historically, simulations used in different fields developed largely independently, but 20th century studies of Systems theory and Cybernetics combined with spreading use of computers across all those fields have led to some unification and a more systematic view of the concept.

use the term for computer simulations modelling selected laws of physics, but this article doesn't)

Interactive simulation is a special kind of physical simulation, often referred to as a Human-in-the-Loop|human in the loop simulation, in which physical simulations include human operators, such as in a flight simulator or a driving simulator.

Human in the loop simulations can include a computer simulation as a so-called synthetic environment.Thales Group|Thales defines synthetic environment as the counterpart to simulated models of sensors, platforms and other active objects for the simulation of the external factors that affect them[ while other vendors use the term for more visual, Virtual Reality-style simulators [

Simulation - Computer simulation
A computer simulation (or sim) is an attempt to model a real-life or hypothetical situation on a computer so that it can be studied to see how the system works. By changing variables in the simulation, predictions may be made about the behaviour of the system. It is a tool to virtually investigate the behaviour of the system under study.

In such simulations, the model (abstract)|model behaviour will change each simulation according to the set of initial parameters assumed for the environment.

There are many different types of computer simulation, the common feature they all share is the attempt to generate a sample of representative scenarios for a model in which a complete enumeration of all possible states would be prohibitive or impossible.

Several software packages exist for running computer-based simulation modeling (e.g. Monte Carlo method|Monte Carlo simulation, stochastic|stochastic modeling, multimethod modeling) that makes all the modeling almost effortless.

Modern usage of the term computer simulation may encompass virtually any computer-based representation.

Simulation - Computer science
Accordingly, in theoretical computer science the term simulation preorder|simulation is a relation between state transition systems, useful in the study of operational semantics.

Since the operation of the computer is simulated, all of the information about the computer's operation is directly available to the programmer, and the speed and execution of the simulation can be varied at will.

Simulators may also be used to interpret fault trees, or test VLSI logic designs before they are constructed. Symbolic simulation uses variables to stand for unknown values.

In the field of Optimization (mathematics)|optimization, simulations of physical processes are often used in conjunction with evolutionary computation to optimize control strategies.

Simulation - Simulation in education and training
Simulation is extensively used for educational purposes. It is frequently used by way of adaptive hypermedia.

There is a distinction, though, between simulations used for training and Instructional Simulation|Instructional simulation.

Training Simulation|Training simulations typically come in one of three categories:Classification used by the Defense Modeling and Simulation Office.

* live simulation (where actual players use genuine systems in a real environment);

* virtual simulation (where actual players use simulated systems in a synthetic environment ), or

* constructive simulation (where simulated players use simulated systems in a synthetic environment). Constructive simulation is often referred to as wargaming since it bears some resemblance to table-top wargaming|war games in which players command armies of soldiers and equipment that move around a board.

In standardized tests, live simulations are sometimes called high-fidelity, producing samples of likely performance, as opposed to low-fidelity, pencil-and-paper simulations producing only signs of possible performance,[ High Versus Low Fidelity Simulations: Does the Type of Format Affect Candidates' Performance or Perceptions?] but the distinction between high, moderate and low fidelity remains relative, depending on the context of a particular comparison.

Other projects for simulations in educations are Open Source Physics, NetSim etc.

Shtub, Simulation-based Learning: The Learning-Forgetting-Relearning Process and Impact of Learning History, Computers Education, April 2008, Vol

Such simulations might be based on fictitious political systems, or be based on current or historical events

In recent years, there has been increasing use of social simulations for staff training in aid and development agencies. The Carana simulation, for example, was first developed by the United Nations Development Programme, and is now used in a very revised form by the World Bank for training staff to deal with fragile and conflict-affected countries.[ Carana, at 'PaxSims' blog, 27 January 2009]

Simulation - Common user interaction systems for virtual simulations
Virtual Simulations use the aforementioned modes of interaction to produce a sense of immersion for the user.

Simulation - Virtual simulation input hardware
There is a wide variety of input hardware available to accept user input for virtual simulations. The following list briefly describes several of them:

'Body tracking' The motion capture method is often used to record the user’s movements and translate the captured data into inputs for the virtual simulation. For example, if a user physically turns their head, the motion would be captured by the simulation hardware in some way and translated to a corresponding shift in view within the simulation.

* Capture suits and/or gloves may be used to capture movements of users body parts. The systems may have sensors incorporated inside them to sense movements of different body parts (e.g., fingers). Alternatively, these systems may have exterior tracking devices or marks that can be detected by external ultrasound, optical receivers or electromagnetic sensors. Internal inertial sensors are also available on some systems. The units may transmit data either wirelessly or through cables.

* Eye trackers can also be used to detect eye movements so that the system can determine precisely where a user is looking at any given instant.

'Physical controllers' Physical controllers provide input to the simulation only through direct manipulation by the user. In virtual simulations, tactile feedback from physical controllers is highly desirable in a number of simulation environments.

* Omni directional treadmills can be used to capture the users locomotion as they walk or run.

* High fidelity instrumentation such as instrument panels in virtual aircraft cockpits provides users with actual controls to raise the level of immersion. For example, pilots can use the actual global positioning system controls from the real device in a simulated cockpit to help them practice procedures with the actual device in the context of the integrated cockpit system.

'Voice/sound recognition' This form of interaction may be used either to interact with agents within the simulation (e.g., virtual people) or to manipulate objects in the simulation (e.g., information). Voice interaction presumably increases the level of immersion for the user.

* Users may use headsets with boom microphones, lapel microphones or the room may be equipped with strategically located microphones.

'Current research into user input systems'

Simulation is a one of the part of an engineering students and also imp for main electrical students its come in form of education purpose.

Simulation - Virtual simulation output hardware
There is a wide variety of output hardware available to deliver stimulus to users in virtual simulations. The following list briefly describes several of them:

'Visual display' Visual displays provide the visual stimulus to the user.

* Stationary displays can vary from a conventional desktop display to 360-degree wrap around screens to stereo three-dimensional screens. Conventional desktop displays can vary in size from 15 to 60+ inches. Wrap around screens are typically utilized in what is known as a Cave Automatic Virtual Environment (CAVE) Cave Automatic Virtual Environment. Stereo three-dimensional screens produce three-dimensional images either with or without special glasses—depending on the design.

Slower update rates tend to cause simulation sickness and disrupt the sense of immersion

'Aural display' Several different types of audio systems exist to help the user hear and localize sounds spatially. Special software can be used to produce 3D audio effects 3D audio to create the illusion that sound sources are placed within a defined three-dimensional space around the user.

* Stationary conventional speaker systems may be used provide dual or multi-channel surround sound. However, external speakers are not as effective as headphones in producing 3D audio effects.

* Conventional headphones offer a portable alternative to stationary speakers. They also have the added advantages of masking real world noise and facilitate more effective 3D audio sound effects.

'Haptic display' These displays provide sense of touch to the user Haptic technology. This type of output is sometimes referred to as force feedback.

* Tactile tile displays use different types of actuators such as inflatable bladders, vibrators, low frequency sub-woofers, pin actuators and/or thermo-actuators to produce sensations for the user.

* End effector displays can respond to users inputs with resistance and force. These systems are often used in medical applications for remote surgeries that employ robotic instruments.Zahraee, A.H., Szewczyk, J., Paik, J.K., Guillaume, M. (2010). Robotic hand-held surgical device: evaluation of end-effector’s kinematics and development of proof-of-concept prototypes. Proceedings of the 13th International Conference on Medical Image Computing and Computer Assisted Intervention, Beijing, China.

They often manifest as motion bases for virtual vehicle simulation such as driving simulators or flight simulators

Simulation - Clinical healthcare simulators
Medical simulators are increasingly being developed and deployed to teach therapeutic and diagnostic procedures as well as medical concepts and decision making to personnel in the health professions

Many medical simulators involve a computer connected to a plastic simulation of the relevant

anatomy. Sophisticated simulators of this type employ a life size mannequin that responds to injected drugs and can be programmed to create simulations of life-threatening emergencies.

Some medical simulations are developed to be widely distributed (such as [ web-enabled simulations] and [ procedural simulations] that can be viewed via standard web browsers) and can be interacted with using standard computer interfaces, such as the computer keyboard|keyboard and computer mouse|mouse.

Another important medical application of a simulator — although, perhaps, denoting a slightly different meaning of simulator — is the use of a placebo drug, a formulation that simulates the active drug in trials of drug efficacy (see Placebo (origins of technical term)).

Simulation - Improving patient safety
According to Building a National Agenda for Simulation-Based Medical Education (Eder-Van Hook, Jackie, 2004), “A health care provider’s ability to react prudently in an unexpected situation is one of the most critical factors in creating a positive outcome in medical emergency, regardless of whether it occurs on the battlefield, freeway, or hospital emergency room.” simulation

Examples of [recently implemented] research simulations used to improve patient care [and its funding] can be found at Improving Patient Safety through Simulation Research (US Department of Human Health Services)

It could be therefore hypothesized that by increasing the number of highly trained residents through the use of simulation training, that the simulation training does in fact increase patient safety

Simulation - History of simulation in healthcare
The first medical simulators were simple models of human patients.

Simulation - History of simulation in healthcare
Since antiquity, these representations in clay and stone were used to demonstrate clinical features of disease states and their effects on humans

Simulation - Type of models
:Active models that attempt to reproduce living anatomy or physiology are recent developments. The famous “Harvey” mannequin was developed at the University of Miami and is able to recreate many of the physical findings of the cardiology examination, including palpation, auscultation, and electrocardiography.

Computer simulations have the advantage of allowing a student to make judgments, and also to make errors

;Computer simulators

:Simulators have been proposed as an ideal tool for assessment of students for clinical skills. For patients, cybertherapy can be used for sessions simulating traumatic expericences, from fear of heights to social anxiety.

These “lifelike” simulations are expensive, and lack reproducibility

:Immersive disease state simulations allow a doctor or HCP to experience what a disease actually feels like. Using sensors and transducers symptomatic effects can be delivered to a participant allowing them to experience the patients disease state.

:Such a simulator meets the goals of an objective and standardized examination for clinical competence. This system is superior to examinations that use Simulated patient|standard patients because it permits the quantitative measurement of competence, as well as reproducing the same objective findings.

Simulation - Simulation in entertainment
Advances in technology in the 1980s and 1990s caused simulation to become more widely used and it began to appear in movies such as Jurassic Park (film)|Jurassic Park (1993) and in computer-based games such as Atari’s Battlezone (1980 video game)|Battlezone (1980).

Simulation - Early history (1940’s and 50’s)
The first simulation game may have been created as early as 1947 by Thomas T

Simulation - Modern simulation (1980’s-present)
Today, computer simulation games such as World of Warcraft are played by millions of people around the world.

Computer-generated imagery was used in film to simulate objects as early as 1976, though in 1982, the film Tron was the first film to use computer-generated imagery for more than a couple of minutes

Simulators have been used for entertainment since the Link Trainer in the 1930s.[ Link Trainer Restoration] The first modern simulator ride to open at a theme park was Disney’s Star Tours in 1987 soon followed by Universal’s The Funtastic World of Hanna-Barbera in 1990 which was the first ride to be done entirely with computer graphics.

Simulation - Computer and video games
There are also Flight Simulation and Driving Simulation games.

Simulation - Film Computer-generated imagery is “the application of the field of 3D computer graphics to special effects”

Simulation - Theme park rides
Today’s simulator rides, such as The Amazing Adventures of Spider-Man include elements to increase the amount of immersion experienced by the riders such as: 3D imagery, physical effects (spraying water or producing scents), and movement through an environment.[ Bringing Spidey to Life: Kleiser-Walczak Construction Company] Examples of simulation rides include

Simulation - Theme park rides
Mission Space and The Simpsons Ride. There are many simulation rides at themeparks like Disney, Universal etc., Examples are Flint Stones, Earth Quake, Time Machine, King Kong.

Simulation - Simulation and manufacturing
Manufacturing represents one of the most important applications of Simulation. This technique represents a valuable tool used by engineers when evaluating the effect of capital investment in equipments and physical facilities like factory plants, warehouses, and distribution centers. Simulation can be used to predict the performance of an existing or planned system and to compare alternative solutions for a particular design problem.

Another important goal of manufacturing-simulations is to quantify system performance. Common measures of system performance include the following:

* System cycle time (how long it take to produce one part);

* Utilization of resource, labor, and machines;

* Queuing at work locations;

Simulation - Automobiles
An automobile simulator provides an opportunity to reproduce the characteristics of real vehicles in a virtual environment. It replicates the external factors and conditions with which a vehicle interacts enabling a driver to feel as if they are sitting in the cab of their own vehicle. Scenarios and events are replicated with sufficient reality to ensure that drivers become fully immersed in the experience rather than simply viewing it as an educational experience.

Simulation - Automobiles
For mature drivers, simulation provides the ability to enhance good driving or to detect poor practice and to suggest the necessary steps for remedial action

Simulation - Biomechanics
An open-source simulation platform for creating dynamic mechanical models built from combinations of rigid and deformable bodies, joints, constraints, and various force actuators. It is specialized for creating biomechanical models of human anatomical structures, with the intention to study their function and eventually assist in the design and planning of medical treatment.

A biomechanics simulator is used to analyze walking dynamics, study sports performance, simulate surgical procedures, analyze joint loads, design medical devices, and animate human and animal movement.

A neuromechanical simulator that combines biomechanical and biologically realistic neural network simulation. It allows the user to test hypotheses on the neural basis of behavior in a physically accurate 3-D virtual environment.

Simulation - City and urban
UrbanSim and Land Use Evolution and Impact Assessment Model|LEAM are examples of large-scale urban simulation models that are used by metropolitan planning agencies and military bases for land use and transportation planning.

Simulation - Classroom of the future
The classroom of the future will be probably contain several kinds of simulators, in addition to textual and visual learning tools

The classroom of the future will also form the basis of a clinical skills unit for continuing education of medical personnel; and in the same way that the use of periodic flight training assists airline pilots, this technology will assist practitioners throughout their career.

The simulator will be more than a living textbook, it will become an integral a part of the practice of medicine. The simulator environment will also provide a standard platform for curriculum development in institutions of medical education.

Simulation - Communication satellites
Simulation is often used in the training of civilian and military personnel

Simulation - Digital Lifecycle
However, for some companies, simulation has not provided the expected benefits.

The research firm Aberdeen Group has found that nearly all best-in-class manufacturers use simulation early in the design process as compared to 3 or 4 laggards who do not.

The ability to use simulation across the entire lifecycle has been enhanced through improved user interfaces such as NX 5#Tailorable UI|tailorable user interfaces and wizards which allow all appropriate PLM participants to take part in the simulation process.

Simulation - Disaster preparedness
Simulation training has become a method for preparing people for disasters. Simulations can replicate emergency situations and track how learners respond thanks to a lifelike experience. Disaster preparedness simulations can involve training on how to handle terrorism attacks, natural disasters, pandemic outbreaks, or other life-threatening emergencies.

As reported by News-Medical.Net, ”The video game is the first in a series of simulations to address bioterrorism, pandemic flu, smallpox and other disasters that emergency personnel must prepare for.News-Medical.Net article- Developed by a team from the University of Illinois at Chicago (UIC), the game allows learners to practice their emergency skills in a safe, controlled environment.

ESP uses simulation to train on the following situations: forest fire fighting, oil or chemical spill response, earthquake response, law enforcement, municipal fire fighting, hazardous material handling, military training, and response to terrorist attack [ Emergency Response Training] One feature of the simulation system is the implementation of “Dynamic Run-Time Clock,” which allows simulations to run a 'simulated' time frame, 'speeding up' or 'slowing down' time as desired” Additionally, the system allows session recordings, picture-icon based navigation, file storage of individual simulations, multimedia components, and launch external applications.

At the University of Québec in Chicoutimi, a research team at the outdoor research and expertise laboratory (Laboratoire d'Expertise et de Recherche en Plein Air - LERPA) specializes in using wilderness backcountry accident simulations to verify emergency response coordination.

Some emergency training simulators also allows for immediate feedback, while other simulations may provide a summary and instruct the learner to engage in the learning topic again.

In a live-emergency situation, emergency responders do not have time to waste. Simulation-training in this environment provides an opportunity for learners to gather as much information as they can and practice their knowledge in a safe environment. They can make mistakes without risk of endangering lives and be given the opportunity to correct their errors to prepare for the real-life emergency.

Simulation - Economics
are the outputs of the simulation

Simulation - Engineering, technology, and processes
Simulation is an important feature in engineering systems or any system that involves many processes

Simulation - Engineering, technology, and processes
Physical and chemical simulations have also direct realistic uses, rather than research uses; in chemical engineering, for example, process simulations are used to give the process parameters immediately used for operating chemical plants, such as oil refineries.

Simulation - Equipment
Often the simulation units will include pre-built scenarios by which to teach trainees, as well as the ability to customize new scenarios

Simulation - Ergonomics
Several ergonomic simulation tools have been developed including Jack, SAFEWORK, RAMSIS, and SAMMIE.

Some simulations also analyze physiological measures including metabolism, energy expenditure, and fatigue limits Cycle time studies, design and process validation, user comfort, reachability, and line of sight are other human-factors that may be examined in ergonomic simulation packages.[ Jack and Process Simulate Human: Siemens PLM Software]

Modeling and simulation of a task can be performed by manually manipulating the virtual human in the simulated environment. Some ergonomics simulation software permits interactive, real-time simulation and evaluation through actual human input via motion capture technologies. However, motion capture for ergonomics requires expensive equipment and the creation of props to represent the environment or product.

The company uses Siemen’s Jack and Jill ergonomics simulation software in improving worker safety and efficiency, without the need to build expensive prototypes.[ From the floor of the 2012 Chicago Auto Show: Automation World shows how Ford uses the power of simulation « Siemens PLM Software Blog]

Simulation - Finance In finance, computer simulations are often used for scenario planning. Risk-adjusted net present value, for example, is computed from well-defined but not always known (or fixed) inputs. By imitating the performance of the project under evaluation, simulation can provide a distribution of NPV over a range of discounts and allowances|discount rates and other variables.

Simulation - Finance As with other industries, the use of simulations can be technology or case-study driven.

Simulation - Flight In comparison to training in an actual aircraft, simulation based training allows for the training of maneuvers or situations that may be impractical (or even dangerous) to perform in the aircraft, while keeping the pilot and instructor in a relatively low-risk environment on the ground

Simulation - Flight For example, conducting multiple instrument approaches in the actual aircraft may require significant time spent repositioning the aircraft, while in a simulation, as soon as one approach has been completed, the instructor can immediately preposition the simulated aircraft to an ideal (or less than ideal) location from which to begin the next approach.

Simulation - Flight Flight simulation also provides an economic advantage over training in an actual aircraft. Once fuel, maintenance, and insurance costs are taken into account, the operating costs of an FSTD are usually substantially lower than the operating costs of the simulated aircraft. For some large transport category airplanes, the operating costs may be several times lower for the FSTD than the actual aircraft.

Simulation - Flight Some are so serious about realistic simulation that they will buy real aircraft parts, like complete nose sections of written-off aircraft, at aircraft boneyards

Simulation - Marine Bearing resemblance to flight simulators, marine simulators train ships' personnel. The most common marine simulators include:

* Ship's bridge simulators
Simulation - Marine * Ship's bridge simulators

* Engine room simulators
Simulation - Marine * Engine room simulators

Simulation - Marine Simulators like these are mostly used within maritime colleges, training institutions and navies. They often consist of a replication of a ships' bridge, with operating console(s), and a number of screens on which the virtual surroundings are projected.

Simulation - Military While many governments make use of simulation, both individually and collaboratively, little is known about the model's specifics outside professional circles.

Simulation - Payment and securities settlement system
Simulation techniques have also been applied to payment and securities settlement systems. Among the main users are central banks who are generally responsible for the oversight of market infrastructure and entitled to contribute to the smooth functioning of the payment systems.

Central banks have been using payment system simulations to evaluate things such as the adequacy or sufficiency of liquidity available ( in the form of account balances and intraday credit limits) to participants (mainly banks) to allow efficient settlement of payments.Leinonen (ed.): Simulation studies of liquidity needs, risks and efficiency in payment networks (Bank of Finland Studies E:39/2007) [ Simulation publications]Neville Arjani: Examining the Trade-Off between Settlement Delay and Intraday Liquidity in Canada's LVTS: A Simulation Approach (Working Paper , Bank of Canada) [ Simulation publications] The need for liquidity is also dependent on the availability and the type of netting procedures in the systems, thus some of the studies have a focus on system comparisons.Johnson, K

Another application is to evaluate risks related to events such as communication network breakdowns or the inability of participants to send payments (e.g. in case of possible bank failure).H. Leinonen (ed.): Simulation analyses and stress testing of payment networks (Bank of Finland Studies E:42/2009) [ Simulation publications] This kind of analysis falls under the concepts of Stress testing or scenario analysis.

A common way to conduct these simulations is to replicate the settlement logics of the real payment or securities settlement systems under analysis and then use real observed payment data. In case of system comparison or system development, naturally also the other settlement logics need to be implemented.

To perform stress testing and scenario analysis, the observed data needs to be altered, e.g. some payments delayed or removed. To analyze the levels of liquidity, initial liquidity levels are varried. System comparisons (benchmarking)or evaluations of new netting algorithms or rules are performed by running simulations with a fixed set of data and varying only the system setups.

Inference is usually done by comparing the benchmark simulation results to the results of altered simulation setups by comparing indicators such as unsettled transactions or settlement delays.

Simulation - Project management
Project management simulation is simulation used for project management training and analysis. It is often used as training simulation for project managers. In other cases it is used for what-if analysis and for supporting decision-making in real projects. Frequently the simulation is conducted using software tools.

Simulation - Robotics A robotics simulator is used to create embedded applications for a specific (or not) robot without being dependent on the 'real' robot

Simulation - Production
There are lots of List of discrete event simulation software|programs commonly used for discrete event simulation

Simulation - Production
There is an academic project investigating the possibilities to use production simulation software for Packaging and labeling|ecology labeling, named EcoProIT.

Simulation - Sales process
Such simulations can help predict the impact of how improvements in methods might impact variability, cost, labor time, and the quantity of transactions at various stages in the process

Simulation - Sports Accuscore, which is licensed by companies such as ESPN, is a well known simulation program for all major sports

Simulation - Sports With the increased interest in fantasy sports simulation models that predict individual player performance have gained popularity. Companies like What If Sports and StatFox specialize in not only using their simulations for predicting game results, but how well individual players will do as well. Many people use models to determine who to start in their fantasy leagues.

Simulation - Sports Another way simulations are helping the sports field is in the use of biomechanics. Models are derived and simulations are run from data received from sensors attached to athletes and video equipment. Sports biomechanics aided by simulation models answer questions regarding training techniques such as: the effect of fatigue on throwing performance (height of throw) and biomechanical factors of the upper limbs (reactive strength index; hand contact time).

Simulation - Sports Computer simulations allow their users to take models which before were too complex to run, and give them answers. Simulations have proven to be some of the best insights into both play performance and team predictability.

Simulation - Space shuttle countdown
The high-level objectives of the Shuttle Final Countdown Phase Simulation are:

* To demonstrate Firing room#Firing room|Firing Room final countdown phase operations.

* To provide training for system engineers in recognizing, reporting and evaluating system problems in a time critical environment.

* To exercise the launch teams ability to evaluate, prioritize and respond to problems in an integrated manner within a time critical environment.

* To provide procedures to be used in performing failure/recovery testing of the operations performed in the final countdown phase.Shuttle Final Countdown Phase Simulation. National Aeronautics and Space Administration KSC Document # RTOMI S0044, Revision AF05, 2009.

Command and control computers, application software, engineering plotting and trending tools, launch countdown procedure documents, launch commit criteria documents, hardware requirement documents, and any other items used by the engineering launch countdown teams during real launch countdown operations are used during the simulation.

Since these math models interact with the command and control application software, models and simulations are also used to debug and verify the functionality of application software.Math Model Main Propulsion System (MPS) Requirements Document, National Aeronautics and Space Administration KSC Document # KSCL , Revision 9, June 2009.

Simulation - Satellite navigation
The only true way to test GNSS receivers (commonly known as Sat-Nav's in the commercial world)is by using an RF Constellation Simulator

Simulation - Weather Predicting weather conditions by extrapolating/interpolating previous data is one of the real use of simulation. Most of the weather forecasts use this information published by Weather buereaus. This kind of simulations help in predicting and forewarning about extreme weather conditions like the path of an active hurricane/cyclone.

Simulation - Weather Numerical weather prediction for forecasting involves complicated numeric computer models to predict weather accurately by taking many parameters into account.

Simulation - Simulation games
Strategy games — both traditional and modern — may be viewed as simulations of abstracted decision-making for the purpose of training military and political leaders (see History of Go for an example of such a tradition, or Kriegsspiel (wargame)|Kriegsspiel for a more recent example).

Simulation - Simulation games
Many other video games are simulators of some kind. Such games can simulate various aspects of reality, from business simulation game|business, to Government simulation|government, to Construction and management simulation games|construction, to Vehicle simulation game|piloting vehicles (see above).

Simulation - Historical usage
Bacon’s essay [ Of Simulation and Dissimulation] expresses somewhat similar views; it is also significant that Samuel Johnson thought so highly of South's definition, that he used it in the entry for simulation in his A Dictionary of the English Language|Dictionary of the English Language.

Flight simulator - Engineering simulation
Engineering flight simulators are used by aerospace manufacturers for such tasks as:

*Developing and testing flight hardware. Simulation (emulation) and stimulation techniques can be used, the latter involving feeding real hardware with artificially generated or real signals (stimulated) in order to verify its operation. Such signals can be electrical, RF, sonar, etc., depending on the equipment to be tested.

*Developing and testing flight software. It is much safer to develop critical flight software on simulators or using simulation techniques than with actual aircraft in flight.

*Developing and testing aircraft systems. For electrical, hydraulic, and flight control systems, full-size engineering rigs, sometimes called 'iron birds', are used during the development of the aircraft and its systems.

Organization studies - Computer simulation
Agent-based modelling and simulation: The potential contribution to organizational psychology

Organization studies - Computer simulation
It is perhaps no accident that those researchers using computational simulation have been inspired by ideas from Mathematical model|biological modeling, ecology, theoretical physics and thermodynamics, chaos theory, complexity theory and organization studies since these methods have also been fruitfully used in those areas.

Game - Simulation The term game can include simulation or re-enactment of various activities or use in real life for various purposes: e.g., training, analysis, prediction

System dynamics - Dynamic simulation results
The dynamic simulation results show that the behaviour of the system would be to have growth in 'adopters' that follows a classical s-curve shape.

System dynamics - Dynamic simulation results
The increase in 'adopters' is very slow initially, then exponential growth for a period, followed ultimately by saturation.

Scientific visualization - Computer simulation
Computer simulations have become a useful part of mathematical modelling of many natural systems in physics, and computational physics, chemistry and biology; human systems in economics, psychology, and social science; and in the process of engineering and new technology, to gain insight into the operation of those systems, or to observe their behavior.

Steven Strogatz (2007). The End of Insight. In: What is your dangerous idea? John Brockman (ed). HarperCollins. The simultaneous visualization and simulation of a system is called visulation.

The scale of events being simulated by computer simulations has far exceeded anything possible (or perhaps even imaginable) using the traditional paper-and-pencil mathematical modeling: over 10 years ago, a desert-battle simulation, of one force invading another, involved the modeling of 66,239 tanks, trucks and other vehicles on simulated terrain around Kuwait, using multiple supercomputers in the United States Department of Defense|DoD High Performance Computer Modernization Program.[ Researchers stage largest military simulation ever]

Nanohub - Simulation tools
The nanoHUB provides in-browser simulation tools geared toward nanotechnology, electrical engineering, chemistry, and semiconductor education. nanoHUB simulations are available to users as both stand-alone tools and part of structured teaching and learning curricula comprising numerous tools. Users develop and contribute their own tools for live deployment.

;SCHRED: calculates envelope wavefunctions and the corresponding bound-state energies in a typical MOSFET|Metal-Oxide-Semiconductor (MOS) or Semiconductor-Oxide-Semiconductor (SOS) structure and a typical SOI structure by solving self-consistently the one-dimensional (1D) Poisson equation and the 1D Schrödinger equation.

;Quantum Dot Lab: computes the eigenstates of a particle in a box of various shapes including domes and pyramids.

;Bulk Monte Carlo Tool: calculates the bulk values of the electron drift velocity, electron average energy and electron mobility for electric fields applied in arbitrary crystallographic direction in both column 4 (Si and Ge) and III-V (GaAs, SiC and GaN) materials.

;Crystal Viewer: helps in visualizing various types of Bravais lattices, planes and Miller indices needed for many material, electronics and chemistry courses. Also large bulk systems for different materials (Silicon, InAs, GaAs, diamond, graphene, Buckyball) can be viewed using this tool.

;Band Structure Lab: uses the sp3s*d5 tight binding method to compute E(k) for bulk, planar, and nanowire semiconductors

;nano-Materials Simulation Toolkit: which uses molecular dynamics to simulate materials at the nano and micro-scale.

;ninithi: which can be used to visualize the 3D molecular geometries of graphene/nano-ribbons,carbon nanotubes and fullerenes. Ninithi also provides features to simulate the electronic band structures of graphene and carbon nanotubes.

Brain simulation 'Computational neuroscience' is the study of brain function in terms of the information processing properties of the structures that make up the nervous system.What is computational neuroscience? Patricia S

Brain simulation Computational neuroscience is distinct from psychological connectionism and from learning theories of disciplines such as machine learning, neural networks, and computational learning theory in that it emphasizes descriptions of functional and biologically realistic neurons (and neural systems) and their physiology and dynamics

Brain simulation These computational models are used to frame hypotheses that can be directly tested by biological and/or psychological experiments.

Brain simulation - History
The term computational neuroscience was introduced by Eric L

Brain simulation - History
The early historical roots of the field can be traced to the work of people such as Louis Lapicque, Alan Hodgkin|Hodgkin Andrew Huxley|Huxley, David H

Brain simulation - Major topics
Research in computational neuroscience can be roughly categorized into several lines of inquiry. Most computational neuroscientists collaborate closely with experimentalists in analyzing novel data and synthesizing new models of biological phenomena.

Brain simulation - Single-neuron modeling
Even single neurons have complex biophysical characteristics

The computational functions of complex dendrites are also under intense investigation. There is a large body of literature regarding how different currents interact with geometric properties of neurons.

Some models are also tracking biochemical pathways at very small scales such as spines or synaptic clefts.

There are many software packages, such as GENESIS (software)|GENESIS and Neuron (software)|NEURON, that allow rapid and systematic in silico modeling of realistic neurons. Blue Brain, a project founded by Henry Markram from the École Polytechnique Fédérale de Lausanne, aims to construct a biophysically detailed simulation of a cortical column on the Blue Gene supercomputer.

Brain simulation - Development, axonal patterning, and guidance
How do axons and dendrites form during development? How do axons know where to target and how to reach these targets? How do neurons migrate to the proper position in the central and peripheral systems? How do synapses form? We know from molecular biology that distinct parts of the nervous system release distinct chemical cues, from growth factors to hormones that modulate and influence the growth and development of functional connections between neurons.

Brain simulation - Development, axonal patterning, and guidance
Theoretical investigations into the formation and patterning of synaptic connection and morphology are still nascent. One hypothesis that has recently garnered some attention is the minimal wiring hypothesis, which postulates that the formation of axons and dendrites effectively minimizes resource allocation while maintaining maximal information storage.Review article

Brain simulation - Sensory processing
Early models of sensory processing understood within a theoretical framework are credited to Horace Barlow. Somewhat similar to the minimal wiring hypothesis described in the preceding section, Barlow understood the processing of the early sensory systems to be a form of efficient coding hypothesis|efficient coding, where the neurons encoded information which minimized the number of spikes. Experimental and computational work have since supported this hypothesis in one form or another.

Brain simulation - Sensory processing
Current research in sensory processing is divided among a biophysical modelling of different subsystems and a more theoretical modelling of perception. Current models of perception have suggested that the brain performs some form of Bayesian inference and integration of different sensory information in generating our perception of the physical world.

Brain simulation - Memory and synaptic plasticity
Earlier models of memory are primarily based on the postulates of Hebbian learning

Brain simulation - Memory and synaptic plasticity
One of the major problems in neurophysiological memory is how it is maintained and changed through multiple time scales

Brain simulation - Behaviors of networks
Biological neurons are connected to each other in a complex, recurrent fashion. These connections are, unlike most artificial neural networks, sparse and usually specific. It is not known how information is transmitted through such sparsely connected networks. It is also unknown what the computational functions of these specific connectivity patterns are, if any.

The interactions of neurons in a small network can be often reduced to simple models such as the Ising model

Models of this type are typically built in large simulation platforms like GENESIS or NEURON]

Brain simulation - Cognition, discrimination, and learning
Computational modeling of higher cognitive functions has only recently begun. Experimental data comes primarily from single-unit recording in primates. The frontal lobe and parietal lobe function as integrators of information from multiple sensory modalities. There are some tentative ideas regarding how simple mutually inhibitory functional circuits in these areas may carry out biologically relevant computation.

The brain seems to be able to discriminate and adapt particularly well in certain contexts. For instance, human beings seem to have an enormous capacity for memorizing and recognizing faces. One of the key goals of computational neuroscience is to dissect how biological systems carry out these complex computations efficiently and potentially replicate these processes in building intelligent machines.

The brain's large-scale organizational principles are illuminated by many fields, including biology, psychology, and clinical practice. Integrative neuroscience attempts to consolidate these observations through unified descriptive models and databases of behavioral measures and recordings. These are the bases for some quantitative modeling of large-scale brain activity.

The Computational Representational Understanding of Mind (CRUM) is another attempt at modeling human cognition through simulated processes like acquired rule-based systems in decision making and the manipulation of visual representations in decision making.

Brain simulation - Consciousness
One of the ultimate goals of psychology/neuroscience is to be able to explain the everyday experience of conscious life. Francis Crick and Christof Koch made some attempts in formulating a consistent framework for future work in neural correlates of consciousness (NCC), though much of the work in this field remains speculative.

ZigBee - Simulation of ZigBee networks
Network simulators, like NS2, OPNET, and NetSim can be used to simulate IEEE ZigBee networks.

ZigBee - Simulation of ZigBee networks
These simulators come with open source C or C++ libraries for users to modify. This way users can check out the validity of new algorithms prior to hardware implementation.

Nick Bostrom - Simulation argument
Bostrom argues that at least one of the following statements is very likely to be true:

# Human civilization is unlikely to reach a level of technological maturity capable of producing simulated reality|simulated realities, or such simulations are physically impossible.

# A comparable civilization reaching aforementioned technological status will likely not produce a significant number of simulated realities, for any of a number of reasons, such as diversion of computational processing power for other tasks, ethical considerations of holding entities captive in simulated realities, etc.

# Any entities with our general set of experiences are almost certainly living in a simulation.

To quantify that tripartite disjunction, he offers the following equation:

Nanoelectromechanical systems - Simulations
Using simulations to predict mechanical and electrical behavior of these devices can help optimize NEMS device design parameters.

Whole brain emulation - Simulation model scale
Since the function of the human mind, and how it might arise from the working of the brain's neural network, are poorly understood issues, mind uploading relies on the idea of neural network emulator|emulation

Whole brain emulation - Simulation of C
Whole brain emulation - Simulation of C. elegans roundworm neural system However, we still lack understanding of how the neurons and the connections between them generate the surprisingly complex range of behaviors that are observed in this relatively simple organism.[ Mark Wakabayashi], with links to MuCoW simulation software, a demo video and the doctoral thesis COMPUTATIONAL PLAUSIBILITY OF STRETCH RECEPTORS AS THE BASIS FOR MOTOR CONTROL IN C

Whole brain emulation - Simulation of C
Whole brain emulation - Simulation of C. elegans roundworm neural system The OpenWorm Project — an open-source project dedicated to creating a virtual C. elegans nematode in a computer by reverse-engineering its biology— has now developed software that replicates the worm’s muscle movement.

Whole brain emulation - Simulation of Drosophila fruit fly neural system
The brain belonging to the fruit fly Drosophila is also thoroughly studied, and to some extent simulated.Arena, P.; Patane, L.; Termini, P.S.; [ An insect brain computational model inspired by Drosophila melanogaster: Simulation results], The 2010 International Joint Conference on Neural Networks (IJCNN)

Whole brain emulation - Rodent brain simulation
An artificial neural network described as being as big and as complex as half of a mouse brain was run on an IBM Blue Gene supercomputer by a University of Nevada research team in A simulated time of one second took ten seconds of computer time. The researchers said they had seen biologically consistent nerve impulses flowed through the virtual cortex. However, the simulation lacked the structures seen in real mice brains, and they intend to improve the accuracy of the neuron model.

The initial goal of the project, completed in December 2006, was the simulation of a rat cortical column|neocortical column, which can be considered the smallest functional unit of the neocortex (the part of the brain thought to be responsible for higher functions such as conscious thought), containing 10,000 neurons (and 108 synapses)

An organization called the [ Brain Preservation Foundation] was founded in 2010 and is offering a Brain Preservation Technology prize to promote exploration of brain preservation technology in service of humanity

Friction - Numerical simulation of the Coulomb model
Despite being a simplified model of friction, the Coulomb model is useful in many Computer simulation|numerical simulation applications such as multibody systems and granular material

Artificial skin - Artificial Microfluidic Skin for In Vitro Perspiration Simulation and Testing
An artificial skin has also been recently demonstrated at the University of Cincinnati for in-vitro sweat simulation and testing, capable of skin-like texture, wetting, sweat pore-density, and sweat rates

Robotics simulator - Robotic Arm Simulation
* [ RoKiSim]: A free educational software for the 3D simulation of six-axis PUMA-type serial robots.

Crystallographic defect - Computer simulation methods
Density-functional theory, classical molecular dynamics and kinetic Monte Carlo

simulations are widely used to study the properties of defects in solids with computer simulations.

Simulating jamming of hard spheres of different sizes and/or in containers with non-commeasurable sizes using the Lubachevsky-Stillinger algorithm

can be an effective technique for demonstrating some types of crystallographic defects.

Social simulation 'Social simulation' is a research field that applies computer|computational methods to study issues in the social sciences. The issues explored include problems in psychology,Hughes, H. P. N., Clegg, C. W., Robinson, M. A., Crowder, R. M. (2012). Agent-based

of Occupational and Organizational Psychology, 85(3), 487–502
Social simulation of Occupational and Organizational Psychology, 85(3), 487–502

Social simulation Social simulation aims to cross the gap between the descriptive approach used in the social sciences and the formal approach used in the hard sciences, by moving the focus on the processes/mechanisms/behaviors that build the social reality.

Social simulation Robert Axelrod regards social simulation as a third way of doing science, differing from both the deductive and inductive approach; generating data that can be analysed inductively, but coming from a rigorously specified set of rules rather than from direct measurement of the real world

Social simulation The social simulation approach to the social sciences is promoted and coordinated by three regional associations, European Social Simulation Association|ESSA for Europe, North America (reorganizing under the new CSSS name), and PAAA [ Pacific Asia].

Social simulation - History and development
The history of the agent based model can be traced back to the Self-replicating machine|Von Neumann machine, a theoretical machine capable of reproduction

Another improvement was brought by mathematician, John Horton Conway|John Conway. He constructed the well-known Conway's Game of Life|Game of Life. Unlike the von Neumann's machine, Conway's Game of Life operated by simple rules in a virtual world in the form of a 2-dimensional checkerboard.

The birth of the agent-based model as a model for social systems was primarily brought about by a computer scientist, Craig Reynolds (computer graphics)|Craig Reynolds. He tried to model the reality of lively biological agents, known as the artificial life, a term coined by Christopher Langton.

Joshua M. Epstein and Robert Axtell developed the first large scale agent model, the Sugarscape, to simulate and explore the role of social phenomena such as seasonal migrations, pollution, sexual reproduction, combat, transmission of disease, and even culture.

Nigel Gilbert published with Klaus G. Troitzsch the first textbook on Social Simulation: Simulation for the Social Scientist (1999) and established its most relevant journal: the Journal of Artificial Societies and Social Simulation.

More recently, Ron Sun developed methods for basing agent-based simulation on models of human cognition, known as cognitive social simulation (see )

Social simulation - Topics
Here are some sample topics that have been explored with social simulation:

* 'norm (social)|Social norms': Robert Axelrod has used simulations to investigate the foundation of morality;Robert Axelrod (1986): [ An Evolutionary Approach to Norms] others have modeled the emergence of norms using memes,Felix Flentge, Daniel Polani and Thomas Uthmann (2001) [ Modelling the Emergence of Possession Norms using Memes] or how social norms and emotions can regulate each other.Alexander Staller and Paolo Petta (2001): [ Introducing Emotions into the Computational Study of Social Norms: A First Evaluation]See Martin Neumann (2008): [ Homo Socionicus: a Case Study of Simulation Models of Norms] for an overview of the recent (as of 2008) research.

* 'Institutions': by investigating under what conditions agents manage to coordinate,José Castro Caldas and Helder Coelho (1999): [ The Origin of Institutions: socio-economic processes, choice, norms and conventions] or by modeling the works of Robert Putnam on civic traditionsDan Miodownik, Britt Cartrite and Ravi Bhavnani (2010): [ Between Replication and Docking: Adaptive Agents, Political Institutions, and Civic Traditions Revisited]

* 'Reputation', for example by making agents with a model of reputation from Pierre Bourdieu (image, social esteem, and prestige) and observing their behavior in a virtual marketplace.Christian Hahn, Bettina Fley, Michael Florian, Daniela Spresny and Klaus Fischer (2007) : [ Social Reputation: a Mechanism for Flexible Self-Regulation of Multiagent Systems]

* 'Knowledge transmission' and the social process of science: there is a [special section on that topic in the Journal of Artificial Societies and Social Simulation[ JASSS vol. 14: Special section: Simulating the Social Processes of Science]

* 'Elections': Kim (2011) has modeled a psychological model of judgement from previous research (notably featuring motivated reasoning), and compared the statistical regularities of the simulation with empirical observations of voter behavior;Sung-youn Kim (2011): [ A Model of Political Judgment: An Agent-Based Simulation of Candidate Evaluation] others have compared delegation methods.Ramzi Suleiman and Ilan Fischer (2000) [ When One Decides for Many: The Effect of Delegation Methods on Cooperation in Simulated Inter-group Conflicts]Marie-Edith Bissey, Mauro Carini and Guido Ortona (2004) [ ALEX3, a Simulation Program to Compare Electoral Systems]

* 'Economics': see computational economics and agent-based computational economics.

Social simulation - Types of simulation and modeling
Social simulation can refer to a general class of strategies for understanding social dynamics using computers to simulate social systems. Social simulation allows for a more systematic way of viewing the possibilities of outcomes.

There are four major types of social simulation:

#System level simulation.

#Agent-based simulation.

Although the subject matter and methodologies in social science differ from those in natural science or computer science, several of the approaches used in contemporary social simulation originated from fields such as physics and Artificial Intelligence.

Social simulation - System level simulation
Navigating through this theoretical simulation will allow researchers to develop educated ideas of what will happen under some specific variables

Social simulation - System level simulation
For example if NASA were to conduct a system level simulation it would benefit the organization by providing a cost effective research method to navigate through the simulation. This allows the researcher to steer through the virtual possibilities of the given simulation and develop safety procedures, and to produce proven facts about how a certain situation will play out.

Social simulation - System level modeling
System level modeling (SLM) aims to specifically predict (unlike system level simulation's generalization in prediction) and convey any number of actions, behaviors, or other theoretical possibilities of nearly any person, object, construct et cetera within a system using a large set of mathematical equations and computer programming in the form of models.

Social simulation - System level modeling
These models, much like simulations, are used to help us better understand specific roles and actions of different things so as to predict behavior and the like.

Social simulation - Agent-based simulation
Agent-based social simulation (ABSS) consists of modeling different societies after artificial agents, (varying on scale) and placing them in a computer simulated society to observe the behaviors of the agents. From this data it is possible to learn about the reactions of the artificial agents and translate them into the results of non-artificial agents and simulations. Three main fields in ABSS are agent-based computing, social science, and computer simulation.

Social simulation - Agent-based simulation
The main purpose of ABSS is to provide models and tools for agent-based simulation of social phenomena

Social simulation - Agent-based modeling
Agent-based modeling (ABM) is a system in which a collection of agents independently interact on networks

Social simulation - Agent-based modeling
Agent-based modeling is an experimental tool for theoretical research. It enables one to deal with more complex individual behaviors, such as adaptation. Overall, through this type of modeling, the creator, or researcher, aims to model behavior of agents and the communication between them in order to better understand how these individual interactions impact an entire population. In essence, ABM is a way of modeling and understanding different global patterns.

Social simulation - Current research
There are several current research projects that relate directly to modeling and agent-based simulation the following are listed below with a brief overview.

*“Generative e-Social Science for Socio-Spatial Simulation” or (GENESIS) is a research node of the UK National Centre for e-Social Science funded by the UK research council [ ESRC]. For further details please see: [ GENESIS Web Page] and [ Blog].

*“National e-Infrastructure for Social Simulation” or (NeISS) is a UK-based project funded by [ JISC]. For further details please see: [ The NeISS Web Pages].

*“Network Models Governance and RD collaboration networks” or (N.E.M.O) is a research centre whose main focus is to identify ways to create and to assess desirable network structures for typical functions; (e.g. knowledge, creation, transfer, and distribution.) This research will ultimately aid policy-makers at all political levels in improving the effectiveness and efficiency of network-based policy instruments at promoting the knowledge economy in Europe.

*“Agent-based Simulations of Market and Consumer Behavior” is another research group that is funded by the Unilever Corporate Research. The current research that is being conducted is investigating the usefulness of agent-based simulations for modeling consumer behavior and to show the potential value and insights it can add to long-established marketing methods.

*“New and Emergent World Models Through Individual, Evolutionary and Social Learning” or (New Ties) is a three-year project that will ultimately create a virtual society developed by agent-based simulation

*Bruch and Mare's project on neighborhood Residential segregation|segregation: The purpose of the study is to figure out the reasoning for neighborhood segregation based on Race (classification of human beings)|race, and to figure out the tipping point (sociology)|tipping point or when people become uncomfortable with the integration levels into their neighborhood, and decide to flee from the neighborhood

*The MAELIA Program (Multi-Agent Emergent Norms Assessment) is a project dealing with the relationships between the users and managers of a natural resource, in that case water, and the related norms and laws that are to be built within them (conventions) or are imposed to them by other actors (institutions). The purpose of the project is to build a generic multiscale platform which is planned to deal with water conflict -related issues.

Agent-based modeling is most useful in providing a bridge between micro and macro levels, which is a large part of what sociology studies

#Emergent structure

#Emergent social order. These studies show how egotism|egoistic adaptation can lead to successful collective action without either altruism or global (top down) imposition of control. A key finding across numerous studies is that the viability of trust, cooperation, and collective action depends decisively on the embeddedness of interaction.

These examples simply show the complexity of our environment and that agent-based models are designed to explore the minimal conditions, the simplest set of assumptions about human behavior, required for a given social phenomenon to emerge at a higher level of organization.

Social simulation - Criticisms
Since its creation, computerized social simulation has been the target of some criticism in regard to its practicality and accuracy. Social simulation's simplification of the complex to form models from which we can better understand the latter is sometimes seen as a draw back, as using fairly simple models to simulate real life with computers is not always the best way to predict behavior.

Most of the criticism seems to be aimed at agent-based models and simulation and how they work:

#Simulations, being man-made from mathematical interfaces, predict human behavior in a far too simple manner in regard to the complexities of humanity and our actions.

#Simulations cannot enlighten researchers as to how people interact or behave in ways not programmed into their models. For this reason, the scope of simulations are limited in that the researchers must already know what they are going to find (to a degree, for they cannot find anything they themselves did not place in the model) at least vaguely, possibly skewing the results.

#Due to the complexities of what is being measured, simulations must be analyzed in unbiased ways; however, with the model running on a pre-made set of instructions coded into it by a modeler, biases exist almost universally.

#It is highly difficult and often impractical to attempt to link the findings from the abstract world the simulation creates and our complex society and all of its variation.

Researchers working in social simulation might respond that the competing theories from the social sciences are far simpler than those achieved through simulation and therefore suffer the aforementioned drawbacks much more strongly

Proteins - Structure prediction and simulation
Complementary to the field of structural genomics, protein structure prediction seeks to develop efficient ways to provide plausible models for proteins whose structures have not yet been determined experimentally

Proteins - Structure prediction and simulation
The processes of protein folding and binding can be simulated using such technique as molecular mechanics, in particular, molecular dynamics and Monte Carlo method|Monte Carlo, which increasingly take advantage of parallel and distributed computing project; molecular modeling on GPU)

Simulation argument The 'simulation hypothesis' ('simulation argument' or 'simulism') proposes that reality is a simulation and those affected are generally unaware of this. The concept is reminiscent of René Descartes' Dieu trompeur|Evil Genius but posits a more futuristic simulated reality.

Simulation argument - Origins
, 2003, [ Are You Living in a Computer Simulation?], Philosophical Quarterly (2003), Vol

Bostrom's trilemma is formulated in 'temporal logic' as follows:[ The Simulation Argument: Why the Probability that You Are Living in a Matrix is Quite High], Nick Bostrom, Professor of Philosophy at Oxford University, 2003

:A technologically mature posthuman civilization would have enormous computing power. Based on this empirical fact, the simulation argument shows that at least one of the following propositions is true:

:# The fraction of human-level civilizations that reach a posthuman stage is very close to zero;

:# The fraction of posthuman civilizations that are interested in running ancestor-simulations is very close to zero;

:# The fraction of all people with our kind of experiences that are living in a simulation is very close to one.

If (3) is true, then we almost certainly live in a simulation

:Unless we are now living in a simulation, our descendants will almost certainly never run an ancestor-simulation.

Chalmers, in The Matrix as Metaphysics agrees that this is not a skeptical hypothesis but rather a Metaphysics|Metaphysical Hypothesis.Davis J. Chalmers [ The Matrix as Metaphysics] Dept of Philosophy, U. o Arizona; paper written for the philosophy section of The Matrix website. Chalmers goes on to identify three separate hypotheses, which, when combined gives what he terms the Matrix Hypothesis; the notion that reality is but a computer simulation:

* The Creationism|Creation Hypothesis, that Physical space-time and its contents were created by beings outside physical space-time It is related to the Omphalos hypothesis in theology.

* The Computational universe theory|Computational Hypothesis, that Microphysical processes throughout space-time are constituted by underlying computational processes

* The Dualism (philosophy of mind)|Mind–Body Hypothesis, that mind is constituted by processes outside physical space-time, and receives its perceptual inputs from and sends its outputs to processes in physical space-time.

The term Simulism appears to have been coined by Ivo Jansch in September 2006.

Simulation argument - Descartes
Descartes (1596–1650) is one of the first 'modern' thinkers to attempt to provide a philosophical framework of mind and the world we perceive around us, seeking a fundamental set of truths

In his work Meditations on First Philosophy, he writes that he can only be sure of one thing: thought exists – cogito ergo sum, normally translated as I think, therefore I am.Descartes, R

Later critics responded to Descartes's 'proof' for the external world with the brain in a vat thought experiment, suggesting in that Descartes' brain might be connected to a machine which simulates all of these perceptions. However, the vat and the machine exist in an external world, so one form of external world is simply replaced by another.

Simulation argument - David Hume
David Hume|Hume (1711–1776) argued for two kinds of reasoning: probable and demonstrative (Hume's fork), and applied these to the Skepticism|skeptical argument that reality is but an illusion

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