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A Comprehensive Review of Simulation
Technology: Development, Methods, Applications,
Challenges and Future Trends
Tao Luan 1
1The Institute of Software, Chinese Academy of Sciences, Beijing 100085, China
luantao@iscas.ac.cn
Abstract—Simulation technology is a comprehensive method
for predicting possible outcomes or evaluating decision-making
effects by establishing computer models of complex systems and
simulating their behavior and evolution processes. This paper
systematically reviews the development history, main methods,
application fields, challenges, and future trends of simulation
technology. Firstly, the article defines the connotation of simu-
lation technology and expounds its important value in military,
business, public policy, and other fields. Secondly, the article
traces the development of simulation technology from its origins
in World War II to the present, summarizing key milestones
such as modeling languages, graphical interfaces, distributed
interaction, and cloud computing. Thirdly, the article introduces
the main modeling paradigms, such as system dynamics, discrete
events, and agents, as well as compound modeling methods
like continuous-discrete hybrid and multi-agent-macro hybrid.
Meanwhile, the article also demonstrates typical applications
of simulation in defense, business, government, engineering,
healthcare, and other fields, using case studies in military oper-
ations, consumer markets, macroeconomics, traffic management,
and disease spread. Based on an analysis of existing technical
challenges, practical dilemmas, and methodological controver-
sies, the article finally outlines the development prospects of
simulation technology in the directions of large-scale high-
performance computing, data-driven modeling, human-computer
hybrid, cognitive-behavioral simulation, and cross-domain cou-
pling. The article points out that simulation is accelerating its
integration with artificial intelligence, big data, digital twins,
and other technologies, evolving towards more intelligent, real-
time, and immersive directions, and will become an indispensable
enabling technology in the digital era. Strengthening theoretical
innovation and application expansion of simulation is of great
significance for enhancing national scientific and technological
strength and comprehensive competitiveness.
Index Terms—Simulation technology, Modeling and simula-
tion, Artificial intelligence, Digital twin, Hybrid modeling
I. INTRODUCTION
Simulation technology is a method of predicting future
results by simulating real-world systems, processes or events.
It has wide applications in military, business, public policy
and other fields, and is an important tool for decision support
and risk assessment. This paper will comprehensively review
the development history, main methods, application examples,
challenges and future trends of simulation technology.
II. DE FIN IT IO N OF SIMULATION TECHNOLOGY
Simulation technology refers to the use of computer models
and simulation tools to simulate the behavior and evolution of
complex systems by setting initial conditions, parameters and
rules, so as to predict possible results or evaluate the effects of
different decision-making schemes [1]. It is a comprehensive
method that combines qualitative analysis with quantitative
calculation, and integrates expert knowledge with data-driven
approaches [2]. The core of simulation technology is to
establish a model that can reflect the key characteristics of
the real system. By adjusting the parameters and variables
of the model, simulation data under different scenarios are
generated to analyze the response of the system to changes
in internal and external factors, assess risks and uncertainties,
and provide decision makers with visualized and interpretable
predictions and decision support [3]. Simulation technology
plays an indispensable role in many fields. In the military do-
main, simulation technology is widely used for weapon equip-
ment demonstration, combat plan evaluation, force deployment
planning, etc., and is the core means of military strategy and
command [4]. In the business domain, simulation technology
can help enterprises forecast market demand, assess investment
risks, optimize supply chain and production processes, and
improve operational efficiency and economic benefits [5]. In
the field of public policy, simulation technology provides a
scientific basis for policy formulation and evaluation, such as
population forecasting, transportation planning, environmental
impact assessment, and health emergency decision-making
[6]. In addition, simulation technology has extensive and in-
depth applications in financial risk management, engineering
design verification, artificial intelligence algorithm testing,
social science research and other fields [7]. With the rapid
development of computer technology and data science, sim-
ulation technology is becoming an increasingly powerful and
universal analysis tool.
III. THE IM PO RTANC E AN D APP LI CATION FIELDS O F
SIMULATION TECHNOLOGY
Simulation technology plays an indispensable role in many
fields. In the military field, simulation technology is widely
used in weapon equipment demonstration, combat plan evalua-
tion, force deployment planning, etc., and is a core component
of military operations research [4]. In the business field,
simulation technology can help enterprises forecast market
demand, assess investment risks, optimize supply chain and
production processes, and improve operational efficiency and
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V olume1, I ssue5International Journal of Emerging Technologies and Advanced Applications May,2024
economic benefits [5]. In the field of public policy, simulation
technology provides a scientific basis for the formulation
and evaluation of policies, such as population forecasting,
transportation planning, environmental impact assessment, and
health emergency decision-making [6]. In addition, simulation
technology has extensive and in-depth applications in financial
risk management, engineering design verification, artificial
intelligence algorithm testing, social science research and other
fields [7]. With the rapid development of computer technol-
ogy and data science, simulation technology is becoming an
increasingly powerful and universal analysis tool.
As shown in Figure 1, the main application fields and
examples of simulation technology are presented.
Fig. 1. Main application fields and examples of simulation technology
IV. HISTORICAL DEV EL OP ME NT O F SIMULATION
TECHNOLOGY
A. Early Methods and Theoretical Foundations
The early ideas of modern simulation technology can be
traced back to the application of operations research in World
War II. With the development of computers in the 1950s,
researchers began to use Monte Carlo simulation to establish
military models [8]. The “Linear Programming-400 Project” of
the U.S. Air Force in 1957 marked the beginning of computer-
aided force analysis [9]. In the 1960s, theories such as game
theory, queuing theory, and inventory theory were introduced
into military simulation [10]. Meanwhile, in the business field,
IBM’s Geofrey Gordon developed the first general-purpose
discrete event simulation language GPSS in 1961 [11]. With
the creation of system dynamics, agent-based modeling, and
object-oriented simulation languages, simulation applications
in various industries continued to deepen [12].
B. Recent Advances and Milestone Events
Since 2014, simulation technology has entered a stage
of rapid development driven by new-generation information
technologies. Table I summarizes the milestone events in
this period. These milestone events reflect the accelerated
TABLE I
IMPORTANT MILESTONES IN THE DEVELOPMENT OF SIMULATION
TECHNOLOGY
Year Milestone Events
2014 US DARPA launched the DDDAS project, promoting the fusion
of simulation and big data [20].
2015 Alibaba’s “Tao” digital twin warehouse simulation system
launched, a benchmark for industrial simulation [21].
2016 AlphaGo defeated top human Go players, showing the potential
of deep reinforcement learning in complex simulations [16].
2017 Gartner listed digital twin as a top ten strategic technology trend,
indicating the convergence of IoT, big data and simulation [22].
2018 AWS launched the SageMaker RL cloud platform, reducing the
threshold for developing and deploying reinforcement learning
applications [23].
2019 IEEE approved the P2807 digital twin system framework stan-
dard project, accelerating the standardization process [24].
2020 The COVID-19 pandemic promoted large-scale application of
computational epidemiology models in public health decisions
[25].
2021 MITRE proposed the ASGS (Actionable Simulation Guidance
System) framework, enhancing the interpretability and trustwor-
thiness of simulation [26].
2022 Shanghai launched a city-level digital twin CIM platform,
integrating simulation, monitoring and intelligent applications
[27].
2023 NVIDIA released the Omniverse platform, providing a founda-
tion for the industrial metaverse based on physically-accurate
simulation and AI [28].
integration of simulation with emerging technologies such as
big data, AI, IoT, and blockchain since 2014, giving birth to
new applications like digital twins and metaverse, and making
simulation a key enabler of the digital transformation.
V. MAIN METHODS AND MO DE LS O F SIMULATION
TECHNOLOGY
A. Traditional Simulation Methods
Traditional simulation methods are mainly based on math-
ematical models and analytical methods, such as differential
equations, stochastic processes, queuing theory, inventory the-
ory, etc. These methods focus on quantitatively describing and
analyzing the macro behavior of the system, and generally
require appropriate simplification and assumptions about the
system [?]. For example, in the military field, Lanchester
equations are widely used to describe the dynamic process
of attrition of combat forces on both sides [?]. In operational
research, optimization methods such as linear programming
and dynamic programming are commonly used for optimal
selection of weapons and equipment and optimal deployment
of troops [?]. The advantages of traditional simulation methods
are relatively simple modeling, high computational efficiency,
and analytical form of results, which are easy to understand.
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However, they are often difficult to finely describe the complex
interactions within the system, and have limited descriptive
power for systems involving intelligent behavioral subjects [?].
B. Modern Simulation Technology
Modern simulation technology mainly refers to a series
of methods based on computers and integrating cutting-
edge achievements in fields such as artificial intelligence and
complexity science. Among them, the individual modeling
and simulation method represented by agent-based modeling
(ABM) has injected new vitality into simulation. ABM gen-
erates the emergent behavior of the system from the micro-
interactions of a large number of heterogeneous intelligent
agents, and can model complex adaptive systems that tradi-
tional methods are difficult to describe [?]. For example, in
financial market simulation, ABM can simulate the game of
investors with different trading strategies and its impact on
market stability [48]. In traffic flow simulation, ABM can
study the self-organizing behavior of vehicles and pedestrians
in road networks [29]. Artificial intelligence technology is
another important driving force for modern simulation. Various
machine learning algorithms are used for behavioral modeling,
environment perception and decision-making of simulation
agents, making simulation more intelligent [?]. Reinforcement
learning allows agents to learn optimal strategies through
continuous trial and error [33]. Deep learning enables agents
to handle high-dimensional perception data, such as learning
to understand battlefield images [47]. Knowledge graphs and
causal reasoning allow agents to make reasonable decisions
based on domain knowledge [39]. In addition, evolutionary
computation, swarm intelligence and other methods have also
been applied in military and socio-economic system modeling
[34]. dx
dt =ky(t)−αx(t)dy
dt =kx(t)−βy(t)(1)
Equation 1: Lanchester’s square law equations, describing the
attrition rate of combat forces x(t)and y(t)on both sides
C. Hybrid Simulation Methods
Hybrid simulation methods refer to integrating multiple
heterogeneous modeling and simulation paradigms within a
unified framework, leveraging their respective strengths to
describe complex systems at multiple levels and dimensions.
Typical hybrid simulation methods include:
(1) Continuous-discrete hybrid simulation, that is, a model
contains both continuous processes and discrete events, such
as the coexistence of continuous material flow and discrete
events such as equipment failures on a production line [40];
(2) System dynamics-discrete event hybrid simulation, the
former depicts the overall structure and feedback loops of the
system, while the latter describes local processes, such as the
macroscopic operation of the supply chain and the microscopic
simulation of order processing [?];
(3) ABM-macro modeling hybrid simulation, such as in
disease transmission models, ABM depicts individual con-
tact behavior, while macro differential equations describe
the susceptible-infected-recovered transition of the population
[36].
Simulation systems with hybrid paradigms have high mod-
eling flexibility and fine-grained multi-scale mapping, but
the complexity and computational cost of modeling increase
accordingly. Constructing a reasonable conceptual framework
to coordinate the semantic interoperability of heterogeneous
models is a challenging problem faced by hybrid modeling
[54].
VI. AP PL IC ATIO N EXA MP LE S OF SIMULATION
TECHNOLOGY
A. Military and Strategic Planning
The military is the earliest, widest and most in-depth
application field of simulation. The DMSO (Defense Modeling
& Simulation Office) under the U.S. Department of Defense
is specifically responsible for military simulation construction,
and has developed a series of large-scale simulation systems
and key technologies [?]. In recent years, the application of
simulation systems has expanded from campaigns and tactics
to military system demonstration, equipment development
demonstration, situation assessment, command information
systems, etc. Figure 2 shows the Gantt chart of the Peninsula
Fig. 2. The Peninsula War simulation is a typical case of the U.S. military
conducting joint combat simulation analysis [?]. The simulation integrates
high-resolution models of multiple services such as land, sea, air, and space,
constructs a refined battlefield environment of the Korean Peninsula and its
surroundings, and sets up multiple confrontation plans for Red and Blue.
Through more than 100,000 simulations, the effect evaluation of different
military action combinations was analyzed, the force deployment and re-
source allocation were optimized, and potential tactical insights were studied,
providing strong support for the planning and decision-making of theater
commanders.
War simulation project plan.
B. Business and Market Forecasting
In the business field, simulation technology is widely used
in supply chain management, production scheduling, mar-
ket forecasting and other aspects. Agent-based modeling is
particularly suitable for simulating the complex interactions
and adaptive behaviors among the many participants in the
market. Procter & Gamble (P&G) has developed an agent-
based consumer market simulation system [29]. The system
builds an artificial market space, and creates virtual consumers,
retailers, competitors and other agents. Each consumer agent
has its own demographic attributes, psychological preferences,
social networks and behavioral rules. They perceive product
information, exchange experiences, and make purchase deci-
sions in the market environment. By adjusting product, price,
channel and promotion strategies, P&G simulated the market
response, forecasted demand, optimized marketing mix, and
supported new product development decisions. The accuracy
of the simulation prediction once reached 90%.
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C. Public Policy and Social Sciences
In the field of public management, simulation is an im-
portant means to assist policy formulation and evaluation. In
recent years, social computing, a new interdisciplinary field
integrating social science and computer science, has emerged,
aiming to understand and tackle complex social problems with
the help of computational simulation methods [30]. The Euro-
pean Union has funded the EURACE (European Agent-based
Computational Economics) project to develop an agent-based
model of the European economy [31]. The model includes
millions of autonomous agents such as households, companies,
banks, and governments, as well as various markets such as la-
bor market, product market, credit market, etc., to simulate the
complex interactions and dynamic evolution of the economic
system. By setting different policy scenarios such as fiscal,
monetary, and regulatory policies, EURACE evaluated the
effects of policies on economic growth, employment, inflation,
and income distribution, providing a “policy wind tunnel”
for EU decision makers. During the European debt crisis,
EURACE provided valuable policy insights.
D. Other Application Areas
In addition to the above typical areas, simulation technology
has been widely used in many other fields in recent years. In
the field of engineering design, simulation is used to verify the
function, performance, safety, reliability and other indicators
of complex systems. For example, in the development of air-
craft, finite element simulation is used to analyze the structural
strength, computational fluid dynamics simulation is used to
study the aerodynamic characteristics, and multi-disciplinary
simulation is used to evaluate the overall performance, greatly
reducing the cost and cycle of physical tests [32]. In the
field of artificial intelligence, simulation provides an important
means for the development and testing of various intelligent
algorithms. For example, in the development of autonomous
driving technology, simulation scenarios are constructed to
train and validate the perception,decision-making and con-
trol modules of driverless cars. Waymo, Baidu Apollo and
other leading companies have developed advanced simulation
platforms, which can generate massive scenes, and support
the testing of algorithms in complex road conditions, extreme
weather and fault scenarios [33]. In the field of social science
research, simulation provides a “computational experimental
platform”. Sociologists construct artificial societies in com-
puters, exploring the emergence of norms, the evolution of
cultures, the spread of public opinion, group behaviors and
other issues [34]. For example, Epstein’s civil violence model
simulated the interaction between rebels and law enforcers,
revealing the effects of factors such as hardship, legitimacy
and repression on the scale of insurgency [35]. The Mentat
project at the University of Zurich aims to simulate the entire
life cycle of the ancient Roman society, from the rise to the
fall of the empire, to reveal the key driving forces of historical
evolution [36].
VII. CHA LL EN GE S AN D LIMITATIONS
A. Technical Challenges
Simulation technology faces many challenges in terms of
technical implementation. One is the “curse of dimensional-
ity”. As the scale and complexity of the simulation system
increase, the state space will explode exponentially. How to
rationally characterize the system, control the model scale,
and ensure the computational efficiency is a dilemma [37].
The second is the validation of the simulation model. Whether
the model can reflect the essence of the real system, whether
the structure and parameters are properly set, and whether the
simulation results are reliable, all need to be strictly validated,
but the workload is huge [38]. In addition, simulation models
often involve a large amount of uncertain information. How to
express and process various types of uncertainty and evaluate
its impact is also a technical difficulty [39].
B. Practical Application Challenges
In practical application scenarios, simulation also faces
many challenges. One is the acceptance of decision makers.
Simulation is a “black box” process for many managers.
They often doubt the reliability of simulation results and are
unwilling to use them as a basis for decision making [40]. The
second is data availability. High-quality data is the cornerstone
of simulation, but in many areas, data is scarce, isolated, or
difficult to access, making it difficult to support simulation
[38]. In addition, the application of simulation also faces
challenges such as high cost, long cycle, professional barriers,
and integration with business processes [37].
C. Methodological Controversies
Simulation, as an emerging technology, still has many
controversies in methodology and epistemology. One view is
that simulation is only an auxiliary tool and cannot replace the
traditional scientific methods such as theoretical analysis and
experimental verification [41]. Another view is that simulation
has created a “new scientific paradigm” that is parallel to
theory and experiment [42]. At the same time, some scholars
question the explanatory power of simulation. They believe
that simulation models, especially agent-based simulation, are
difficult to reveal the causal mechanism, and often stay at the
level of correlation and phenomenon reappearance [43].
VIII. FUTURE TRE ND S AN D PROS PE CT S
A. Technological Innovation
The development of a new generation of information tech-
nology will further promote the innovation of simulation
technology. The new generation of AI represented by deep
learning and reinforcement learning will give simulation sys-
tems stronger autonomous learning and complex decision-
making capabilities [44]. Digital twin, CIM engineering and
other technologies will make simulation models more real-
time and consistent with physical entities [45]. VR/AR and
other emerging interaction technologies will also bring more
immersive simulation experience. In addition, the development
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of cloud computing, big data, blockchain and other new
infrastructure will provide strong computing, data and credible
support for the implementation of simulation applications [46].
B. Application Field Expansion
In the future, simulation technology will be more widely
used in more fields. In the military field, with the develop-
ment of intelligent warfare, simulation will penetrate into the
whole chain of military activities such as strategy, weapon
development, tactical deduction, and actual combat [47]. In
the economic field, central banks and financial institutions are
digital economic systems” to assist macro-control and financial
supervision [48]. In the field of smart cities, city simulators”
will become an important tool for urban planning, operation
management and emergency response [49]. In addition, sim-
ulation will also have great application potential in the fields
of smart healthcare, education and training, entertainment and
media.
C. Research Directions
In the future, the research and innovation of simulation
technology will focus on the following directions:
(1) Large-scale, high-performance simulation method.
Research on parallel, distributed, and cloud-based simulation
frameworks to improve computing efficiency [50].
(2) Data-driven modeling and simulation. Use machine
learning, data assimilation and other methods to directly mine
simulation models from data and combine knowledge-driven
with data-driven [51].
(3) Human-machine hybrid simulation. Integrate human
experts into the simulation loop, leveraging human
experience and machine capabilities to achieve human-
machine collaboration [52].
(4) Cognitive and behavioral modeling. From the modeling
of simple behaviors to the simulation of advanced cognition
such as emotion, learning and creativity [53].
(5) Cross-domain composite simulation. Break down
disciplinary barriers and couple multi-disciplinary, multi-
domain and multi-level simulation models [54].
IX. CONCLUSION
This paper comprehensively reviews the development
course, main methods, typical applications, existing challenges
and future trends of simulation technology. As an important
means of understanding the world and assisting decision-
making, simulation technology has developed vigorously in
both military and civilian fields, and has produced huge eco-
nomic and social benefits. At present, simulation technology
is ushering in a new round of revolution under the background
of a new generation of information technology. Simulation is
integrating with emerging technologies such as artificial intel-
ligence, big data, and digital twins, and is evolving towards
a more intelligent, real-time, and immersive direction. It is
foreseeable that in the future, simulation technology will be
more widely used in various economic and social fields, and
will become an indispensable tool for humanity’s ability to
understand and shape the world around us. Therefore, strength-
ening the research on the theory and methods of simulation
technology, promoting the development and application of
simulation technology, is of great significance for enhancing
the country’s scientific and technological innovation capability
and comprehensive national strength.
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