A Comprehensive Review of Simulation

Technology: Development, Methods, Applications,

Challenges and Future Trends
Tao Luan 1
1 The Institute of Software, Chinese Academy of Sciences, Beijing 100085, China
luantao@iscas.ac.cn

Abstract—Simulation technology is a comprehensive method complex systems by setting initial conditions, parameters and
for predicting possible outcomes or evaluating decision-making rules, so as to predict possible results or evaluate the effects of
effects by establishing computer models of complex systems and different decision-making schemes [1]. It is a comprehensive
simulating their behavior and evolution processes. This paper
systematically reviews the development history, main methods, method that combines qualitative analysis with quantitative
application fields, challenges, and future trends of simulation calculation, and integrates expert knowledge with data-driven
technology. Firstly, the article defines the connotation of simu- approaches [2]. The core of simulation technology is to
lation technology and expounds its important value in military, establish a model that can reflect the key characteristics of
business, public policy, and other fields. Secondly, the article the real system. By adjusting the parameters and variables
traces the development of simulation technology from its origins
in World War II to the present, summarizing key milestones of the model, simulation data under different scenarios are
such as modeling languages, graphical interfaces, distributed generated to analyze the response of the system to changes
interaction, and cloud computing. Thirdly, the article introduces in internal and external factors, assess risks and uncertainties,
the main modeling paradigms, such as system dynamics, discrete and provide decision makers with visualized and interpretable
events, and agents, as well as compound modeling methods predictions and decision support [3]. Simulation technology
like continuous-discrete hybrid and multi-agent-macro hybrid.
Meanwhile, the article also demonstrates typical applications plays an indispensable role in many fields. In the military do-
of simulation in defense, business, government, engineering, main, simulation technology is widely used for weapon equip-
healthcare, and other fields, using case studies in military oper- ment demonstration, combat plan evaluation, force deployment
ations, consumer markets, macroeconomics, traffic management, planning, etc., and is the core means of military strategy and
and disease spread. Based on an analysis of existing technical command [4]. In the business domain, simulation technology
challenges, practical dilemmas, and methodological controver-
sies, the article finally outlines the development prospects of can help enterprises forecast market demand, assess investment
simulation technology in the directions of large-scale high- risks, optimize supply chain and production processes, and
performance computing, data-driven modeling, human-computer improve operational efficiency and economic benefits [5]. In
hybrid, cognitive-behavioral simulation, and cross-domain cou- the field of public policy, simulation technology provides a
pling. The article points out that simulation is accelerating its scientific basis for policy formulation and evaluation, such as
integration with artificial intelligence, big data, digital twins,
and other technologies, evolving towards more intelligent, real- population forecasting, transportation planning, environmental
time, and immersive directions, and will become an indispensable impact assessment, and health emergency decision-making
enabling technology in the digital era. Strengthening theoretical [6]. In addition, simulation technology has extensive and in-
innovation and application expansion of simulation is of great depth applications in financial risk management, engineering
significance for enhancing national scientific and technological design verification, artificial intelligence algorithm testing,
strength and comprehensive competitiveness.
Index Terms—Simulation technology, Modeling and simula- social science research and other fields [7]. With the rapid
tion, Artificial intelligence, Digital twin, Hybrid modeling development of computer technology and data science, sim-
ulation technology is becoming an increasingly powerful and
I. I NTRODUCTION universal analysis tool.
Simulation technology is a method of predicting future
III. T HE I MPORTANCE AND A PPLICATION F IELDS OF
results by simulating real-world systems, processes or events.
S IMULATION T ECHNOLOGY
It has wide applications in military, business, public policy
and other fields, and is an important tool for decision support Simulation technology plays an indispensable role in many
and risk assessment. This paper will comprehensively review fields. In the military field, simulation technology is widely
the development history, main methods, application examples, used in weapon equipment demonstration, combat plan evalua-
challenges and future trends of simulation technology. tion, force deployment planning, etc., and is a core component
of military operations research [4]. In the business field,
II. D EFINITION OF S IMULATION T ECHNOLOGY simulation technology can help enterprises forecast market
Simulation technology refers to the use of computer models demand, assess investment risks, optimize supply chain and
and simulation tools to simulate the behavior and evolution of production processes, and improve operational efficiency and

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economic benefits [5]. In the field of public policy, simulation object-oriented simulation languages, simulation applications
technology provides a scientific basis for the formulation in various industries continued to deepen [12].
and evaluation of policies, such as population forecasting,
transportation planning, environmental impact assessment, and B. Recent Advances and Milestone Events
health emergency decision-making [6]. In addition, simulation Since 2014, simulation technology has entered a stage
technology has extensive and in-depth applications in financial of rapid development driven by new-generation information
risk management, engineering design verification, artificial technologies. Table I summarizes the milestone events in
intelligence algorithm testing, social science research and other this period. These milestone events reflect the accelerated
fields [7]. With the rapid development of computer technol-
ogy and data science, simulation technology is becoming an TABLE I
increasingly powerful and universal analysis tool. I MPORTANT MILESTONES IN THE DEVELOPMENT OF SIMULATION
TECHNOLOGY
As shown in Figure 1, the main application fields and
examples of simulation technology are presented. 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.
Fig. 1. Main application fields and examples of simulation technology V. M AIN M ETHODS AND M ODELS OF S IMULATION
T ECHNOLOGY

IV. H ISTORICAL D EVELOPMENT OF S IMULATION A. Traditional Simulation Methods

T ECHNOLOGY Traditional simulation methods are mainly based on math-
ematical models and analytical methods, such as differential
A. Early Methods and Theoretical Foundations
equations, stochastic processes, queuing theory, inventory the-
The early ideas of modern simulation technology can be ory, etc. These methods focus on quantitatively describing and
traced back to the application of operations research in World analyzing the macro behavior of the system, and generally
War II. With the development of computers in the 1950s, require appropriate simplification and assumptions about the
researchers began to use Monte Carlo simulation to establish system [?]. For example, in the military field, Lanchester
military models [8]. The “Linear Programming-400 Project” of equations are widely used to describe the dynamic process
the U.S. Air Force in 1957 marked the beginning of computer- of attrition of combat forces on both sides [?]. In operational
aided force analysis [9]. In the 1960s, theories such as game research, optimization methods such as linear programming
theory, queuing theory, and inventory theory were introduced and dynamic programming are commonly used for optimal
into military simulation [10]. Meanwhile, in the business field, selection of weapons and equipment and optimal deployment
IBM’s Geofrey Gordon developed the first general-purpose of troops [?]. The advantages of traditional simulation methods
discrete event simulation language GPSS in 1961 [11]. With are relatively simple modeling, high computational efficiency,
the creation of system dynamics, agent-based modeling, and and analytical form of results, which are easy to understand.

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However, they are often difficult to finely describe the complex the susceptible-infected-recovered transition of the population
interactions within the system, and have limited descriptive [36].
power for systems involving intelligent behavioral subjects [?]. Simulation systems with hybrid paradigms have high mod-
eling flexibility and fine-grained multi-scale mapping, but
B. Modern Simulation Technology the complexity and computational cost of modeling increase
Modern simulation technology mainly refers to a series accordingly. Constructing a reasonable conceptual framework
of methods based on computers and integrating cutting- to coordinate the semantic interoperability of heterogeneous
edge achievements in fields such as artificial intelligence and models is a challenging problem faced by hybrid modeling
complexity science. Among them, the individual modeling [54].
and simulation method represented by agent-based modeling
VI. A PPLICATION E XAMPLES OF S IMULATION
(ABM) has injected new vitality into simulation. ABM gen-
T ECHNOLOGY
erates the emergent behavior of the system from the micro-
interactions of a large number of heterogeneous intelligent A. Military and Strategic Planning
agents, and can model complex adaptive systems that tradi- The military is the earliest, widest and most in-depth
tional methods are difficult to describe [?]. For example, in application field of simulation. The DMSO (Defense Modeling
financial market simulation, ABM can simulate the game of & Simulation Office) under the U.S. Department of Defense
investors with different trading strategies and its impact on is specifically responsible for military simulation construction,
market stability [48]. In traffic flow simulation, ABM can and has developed a series of large-scale simulation systems
study the self-organizing behavior of vehicles and pedestrians and key technologies [?]. In recent years, the application of
in road networks [29]. Artificial intelligence technology is simulation systems has expanded from campaigns and tactics
another important driving force for modern simulation. Various to military system demonstration, equipment development
machine learning algorithms are used for behavioral modeling, demonstration, situation assessment, command information
environment perception and decision-making of simulation systems, etc. Figure 2 shows the Gantt chart of the Peninsula
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 Fig. 2. The Peninsula War simulation is a typical case of the U.S. military
based on domain knowledge [39]. In addition, evolutionary conducting joint combat simulation analysis [?]. The simulation integrates
high-resolution models of multiple services such as land, sea, air, and space,
computation, swarm intelligence and other methods have also constructs a refined battlefield environment of the Korean Peninsula and its
been applied in military and socio-economic system modeling surroundings, and sets up multiple confrontation plans for Red and Blue.
[34]. Through more than 100,000 simulations, the effect evaluation of different
dx dy military action combinations was analyzed, the force deployment and re-
= ky(t) − αx(t) = kx(t) − βy(t) (1) source allocation were optimized, and potential tactical insights were studied,
dt dt providing strong support for the planning and decision-making of theater
Equation 1: Lanchester’s square law equations, describing the commanders.
attrition rate of combat forces x(t) and y(t) on both sides War simulation project plan.
C. Hybrid Simulation Methods B. Business and Market Forecasting
Hybrid simulation methods refer to integrating multiple In the business field, simulation technology is widely used
heterogeneous modeling and simulation paradigms within a in supply chain management, production scheduling, mar-
unified framework, leveraging their respective strengths to ket forecasting and other aspects. Agent-based modeling is
describe complex systems at multiple levels and dimensions. particularly suitable for simulating the complex interactions
Typical hybrid simulation methods include: and adaptive behaviors among the many participants in the
(1) Continuous-discrete hybrid simulation, that is, a model market. Procter & Gamble (P&G) has developed an agent-
contains both continuous processes and discrete events, such based consumer market simulation system [29]. The system
as the coexistence of continuous material flow and discrete builds an artificial market space, and creates virtual consumers,
events such as equipment failures on a production line [40]; retailers, competitors and other agents. Each consumer agent
(2) System dynamics-discrete event hybrid simulation, the has its own demographic attributes, psychological preferences,
former depicts the overall structure and feedback loops of the social networks and behavioral rules. They perceive product
system, while the latter describes local processes, such as the information, exchange experiences, and make purchase deci-
macroscopic operation of the supply chain and the microscopic sions in the market environment. By adjusting product, price,
simulation of order processing [?]; channel and promotion strategies, P&G simulated the market
(3) ABM-macro modeling hybrid simulation, such as in response, forecasted demand, optimized marketing mix, and
disease transmission models, ABM depicts individual con- supported new product development decisions. The accuracy
tact behavior, while macro differential equations describe of the simulation prediction once reached 90%.

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C. Public Policy and Social Sciences VII. C HALLENGES AND L IMITATIONS

In the field of public management, simulation is an im- A. Technical Challenges

portant means to assist policy formulation and evaluation. In Simulation technology faces many challenges in terms of
recent years, social computing, a new interdisciplinary field technical implementation. One is the “curse of dimensional-
integrating social science and computer science, has emerged, ity”. As the scale and complexity of the simulation system
aiming to understand and tackle complex social problems with increase, the state space will explode exponentially. How to
the help of computational simulation methods [30]. The Euro- rationally characterize the system, control the model scale,
pean Union has funded the EURACE (European Agent-based and ensure the computational efficiency is a dilemma [37].
Computational Economics) project to develop an agent-based The second is the validation of the simulation model. Whether
model of the European economy [31]. The model includes the model can reflect the essence of the real system, whether
millions of autonomous agents such as households, companies, the structure and parameters are properly set, and whether the
banks, and governments, as well as various markets such as la- simulation results are reliable, all need to be strictly validated,
bor market, product market, credit market, etc., to simulate the but the workload is huge [38]. In addition, simulation models
complex interactions and dynamic evolution of the economic often involve a large amount of uncertain information. How to
system. By setting different policy scenarios such as fiscal, express and process various types of uncertainty and evaluate
monetary, and regulatory policies, EURACE evaluated the its impact is also a technical difficulty [39].
effects of policies on economic growth, employment, inflation,
and income distribution, providing a “policy wind tunnel” B. Practical Application Challenges
for EU decision makers. During the European debt crisis, In practical application scenarios, simulation also faces
EURACE provided valuable policy insights. 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
D. Other Application Areas unwilling to use them as a basis for decision making [40]. The
In addition to the above typical areas, simulation technology second is data availability. High-quality data is the cornerstone
has been widely used in many other fields in recent years. In of simulation, but in many areas, data is scarce, isolated, or
the field of engineering design, simulation is used to verify the difficult to access, making it difficult to support simulation
function, performance, safety, reliability and other indicators [38]. In addition, the application of simulation also faces
of complex systems. For example, in the development of air- challenges such as high cost, long cycle, professional barriers,
craft, finite element simulation is used to analyze the structural and integration with business processes [37].
strength, computational fluid dynamics simulation is used to C. Methodological Controversies
study the aerodynamic characteristics, and multi-disciplinary
simulation is used to evaluate the overall performance, greatly Simulation, as an emerging technology, still has many
reducing the cost and cycle of physical tests [32]. In the controversies in methodology and epistemology. One view is
field of artificial intelligence, simulation provides an important that simulation is only an auxiliary tool and cannot replace the
means for the development and testing of various intelligent traditional scientific methods such as theoretical analysis and
algorithms. For example, in the development of autonomous experimental verification [41]. Another view is that simulation
driving technology, simulation scenarios are constructed to has created a “new scientific paradigm” that is parallel to
train and validate the perception,decision-making and con- theory and experiment [42]. At the same time, some scholars
trol modules of driverless cars. Waymo, Baidu Apollo and question the explanatory power of simulation. They believe
other leading companies have developed advanced simulation that simulation models, especially agent-based simulation, are
platforms, which can generate massive scenes, and support difficult to reveal the causal mechanism, and often stay at the
the testing of algorithms in complex road conditions, extreme level of correlation and phenomenon reappearance [43].
weather and fault scenarios [33]. In the field of social science VIII. F UTURE T RENDS AND P ROSPECTS
research, simulation provides a “computational experimental
platform”. Sociologists construct artificial societies in com- A. Technological Innovation
puters, exploring the emergence of norms, the evolution of The development of a new generation of information tech-
cultures, the spread of public opinion, group behaviors and nology will further promote the innovation of simulation
other issues [34]. For example, Epstein’s civil violence model technology. The new generation of AI represented by deep
simulated the interaction between rebels and law enforcers, learning and reinforcement learning will give simulation sys-
revealing the effects of factors such as hardship, legitimacy tems stronger autonomous learning and complex decision-
and repression on the scale of insurgency [35]. The Mentat making capabilities [44]. Digital twin, CIM engineering and
project at the University of Zurich aims to simulate the entire other technologies will make simulation models more real-
life cycle of the ancient Roman society, from the rise to the time and consistent with physical entities [45]. VR/AR and
fall of the empire, to reveal the key driving forces of historical other emerging interaction technologies will also bring more
evolution [36]. immersive simulation experience. In addition, the development

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of cloud computing, big data, blockchain and other new will become an indispensable tool for humanity’s ability to
infrastructure will provide strong computing, data and credible understand and shape the world around us. Therefore, strength-
support for the implementation of simulation applications [46]. ening the research on the theory and methods of simulation
technology, promoting the development and application of
B. Application Field Expansion simulation technology, is of great significance for enhancing
In the future, simulation technology will be more widely the country’s scientific and technological innovation capability
used in more fields. In the military field, with the develop- and comprehensive national strength.
ment of intelligent warfare, simulation will penetrate into the
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