Data Science: Simulations (Just the Quotes)

"The mathematical and computing techniques for making programmed decisions replace man but they do not generally simulate him." (Herbert A Simon, "Corporations 1985", 1960)

"The main object of cybernetics is to supply adaptive, hierarchical models, involving feedback and the like, to all aspects of our environment. Often such modelling implies simulation of a system where the simulation should achieve the object of copying both the method of achievement and the end result. Synthesis, as opposed to simulation, is concerned with achieving only the end result and is less concerned (or completely unconcerned) with the method by which the end result is achieved. In the case of behaviour, psychology is concerned with simulation, while cybernetics, although also interested in simulation, is primarily concerned with synthesis." (Frank H George, "Soviet Cybernetics, the militairy and Professor Lerner", New Scientist, 1973)

"Computer based simulation is now in wide spread use to analyse system models and evaluate theoretical solutions to observed problems. Since important decisions must rely on simulation, it is essential that its validity be tested, and that its advocates be able to describe the level of authentic representation which they achieved." (Richard Hamming, 1975)

"When a real situation involves chance we have to use probability mathematics to understand it quantitatively. Direct mathematical solutions sometimes exist […] but most real systems are too complicated for direct solutions. In these cases the computer, once taught to generate random numbers, can use simulation to get useful answers to otherwise impossible problems." (Robert Hooke, "How to Tell the Liars from the Statisticians", 1983)

"The real leverage in most management situations lies in understanding dynamic complexity, not detail complexity. […] Unfortunately, most 'systems analyses' focus on detail complexity not dynamic complexity. Simulations with thousands of variables and complex arrays of details can actually distract us from seeing patterns and major interrelationships. In fact, sadly, for most people 'systems thinking' means 'fighting complexity with complexity', devising increasingly 'complex' (we should really say 'detailed') solutions to increasingly 'complex' problems. In fact, this is the antithesis of real systems thinking." (Peter M Senge, "The Fifth Discipline: The Art and Practice of the Learning Organization", 1990)

"A model for simulating dynamic system behavior requires formal policy descriptions to specify how individual decisions are to be made. Flows of information are continuously converted into decisions and actions. No plea about the inadequacy of our understanding of the decision-making processes can excuse us from estimating decision-making criteria. To omit a decision point is to deny its presence - a mistake of far greater magnitude than any errors in our best estimate of the process." (Jay W Forrester, "Policies, decisions and information sources for modeling", 1994)

"A field of study that includes a methodology for constructing computer simulation models to achieve better under-standing of social and corporate systems. It draws on organizational studies, behavioral decision theory, and engineering to provide a theoretical and empirical base for structuring the relationships in complex systems." (Virginia Anderson & Lauren Johnson, "Systems Thinking Basics: From Concepts to Casual Loops", 1997)

"What it means for a mental model to be a structural analog is that it embodies a representation of the spatial and temporal relations among, and the causal structures connecting the events and entities depicted and whatever other information that is relevant to the problem-solving talks. […] The essential points are that a mental model can be nonlinguistic in form and the mental mechanisms are such that they can satisfy the model-building and simulative constraints necessary for the activity of mental modeling." (Nancy J Nersessian, "Model-based reasoning in conceptual change", 1999)

"A neural network is a particular kind of computer program, originally developed to try to mimic the way the human brain works. It is essentially a computer simulation of a complex circuit through which electric current flows." (Keith J Devlin & Gary Lorden, "The Numbers behind NUMB3RS: Solving crime with mathematics", 2007)

"[...] a model is a tool for taking decisions and any decision taken is the result of a process of reasoning that takes place within the limits of the human mind. So, models have eventually to be understood in such a way that at least some layer of the process of simulation is comprehensible by the human mind. Otherwise, we may find ourselves acting on the basis of models that we don’t understand, or no model at all.” (Ugo Bardi, “The Limits to Growth Revisited”, 2011)

"Not only the mathematical way of thinking, but also simulations assisted by mathematical methods, is quite effective in solving problems. The latter is utilized in various fields, including detection of causes of troubles, optimization of expected performances, and best possible adjustments of usage conditions. Conversely, without the aid of mathematical methods, our problem-solving effort will get stuck most probably [...]" (Shiro Hiruta, "Mathematics Contributing to Innovation of Management", [in "What Mathematics Can Do for You"] 2013)

"System dynamics [...] uses models and computer simulations to understand behavior of an entire system, and has been applied to the behavior of large and complex national issues. It portrays the relationships in systems as feedback loops, lags, and other descriptors to explain dynamics, that is, how a system behaves over time. Its quantitative methodology relies on what are called 'stock-and-flow diagrams' that reflect how levels of specific elements accumulate over time and the rate at which they change. Qualitative systems thinking constructs evolved from this quantitative discipline." (Karen L Higgins, "Economic Growth and Sustainability: Systems Thinking for a Complex World", 2015)

"Optimization is more than finding the best simulation results. It is itself a complex and evolving field that, subject to certain information constraints, allows data scientists, statisticians, engineers, and traders alike to perform reality checks on modeling results." (Chris Conlan, "Automated Trading with R: Quantitative Research and Platform Development", 2016)

"But [bootstrap-based] simulations are clumsy and time-consuming, especially with large data sets, and in more complex circumstances it is not straightforward to work out what should be simulated. In contrast, formulae derived from probability theory provide both insight and convenience, and always lead to the same answer since they don’t depend on a particular simulation. But the flip side is that this theory relies on assumptions, and we should be careful not to be deluded by the impressive algebra into accepting unjustified conclusions." (David Spiegelhalter, "The Art of Statistics: Learning from Data", 2019)

Data Science: Simulation Model (Definitions)

"A 'what-if' model that attempts to simulate the effects of alternative management policies and assumptions about the firm's external environment. It is basically a tool for management's laboratory." (Jae K Shim & Joel G Siegel, "Budgeting Basics and Beyond", 2008)

"Simulation models are formal representations of a portion of reality. Simulation models allow managers to share and test assumptions about problem causes and solutions." (Luis F Luna-Reyes, "System Dynamics to Understand Public Information Technology", 2008)

"A simplified, computer, simulation-based construction (model) of some real world phenomenon (or the problem task)." (Hassan Qudrat-Ullah, "System Dynamics Based Technology for Decision Support", 2009)

"A model that shows the expected operation of a system based solely on the model." (DAMA International, "The DAMA Dictionary of Data Management", 2011)

"An analytical technique that often involves running models repeatedly using a variety of inputs to determine the upper and lower bounds of possible outcomes. This simulation process is also sometimes used to identify the likely distribution of outputs given a series of assumptions around how the inputs are distributed." (Evan Stubbs, "Delivering Business Analytics: Practical Guidelines for Best Practice", 2013)

"A representation of a system that can be used to mimic the processes of the system under varying circumstances. It is usually operated subject to stochastic disturbances." (Özgür Yalçınkaya, "A General Simulation Modelling Framework for Train Timetabling Problem", 2016)

"A model that represents an actual procedure over time." (Rania Tegou, "Excess Inventories and Stock Out Events Through Advanced Demand Analysis and Emergency Deliveries",  2018)

"technique that created a detailed model to predict the behavior of CI/service" (ITIL)

Data Science: Monte Carlo Simulation (Definitions)

"A computer-simulation technique that uses sampling from a random number sequence to simulate characteristics or events or outcomes with multiple possible values." (Clyde M Creveling, "Six Sigma for Technical Processes: An Overview for R Executives, Technical Leaders, and Engineering Managers", 2006)

"A simulation in which random events are modeled using pseudo random number generators so that many replications of the random events may be evaluated statistically." (Norman Pendegraft & Mark Rounds, "Dynamic System Simulation for Decision Support", 2008)

"A range of computational algorithms that generates random samples from distributions with known overall properties that is used, for example, to explore potential future behaviours of financial instruments on the basis of historic properties." (Bin Li & Lee Gillam, "Grid Service Level Agreements Using Financial Risk Analysis Techniques", 2010)

"A process which generates hundreds or thousands of probable performance outcomes based on probability distributions for cost and schedule on individual tasks. The outcomes are then used to generate a probability distribution for the project as a whole." (Cynthia Stackpole, "PMP® Certification All-in-One For Dummies®", 2011)

"The technique used by project management applications to estimate the likely range of outcomes from a complex random process by simulating the process a large number of times." (Christopher Carson et al, "CPM Scheduling for Construction: Best Practices and Guidelines", 2014)

"A method for estimating uncertainty in a variable which is a complex function of one or more probability distributions; it uses random numbers to provide an estimate of the distribution and a random number generator to produce random samples from the probabilistic levels." (María C Carnero, "Benchmarking of the Maintenance Service in Health Care Organizations", 2017)

"An analysis technique where a computer model is iterated many times, with the input values chosen at random for each iteration driven by the input data, including probability distributions and probabilistic branches. Outputs are generated to represent the range of possible outcomes for the project." (Project Management Institute, "A Guide to the Project Management Body of Knowledge (PMBOK® Guide )", 2017)

"A computerized simulation technique which is usually used for analyzing the behaviour of a system or a process involving uncertainties." (Henry Xu & Renae Agrey, "Major Techniques and Current Developments of Supply Chain Process Modelling", 2019)

"'What if' analysis of the future project scenarios, provided a mathematical/ logical model of the project implemented on a computer." (Franco Caron, "Project Control Using a Bayesian Approach", 2019)

Data Science: Simulation (Definitions)

"A computer model of part of a real-world system." (Jesse Liberty, "Sams Teach Yourself C++ in 24 Hours" 3rd Ed., 2001)

"An interactive environment in which features in the environment behave similarly to real-world events." (Ruth C Clark & Chopeta Lyons, "Graphics for Learning", 2004)

"An attempt to represent a real life system via a model to determine how a change in one or more variable affects the rest of the system. It is also called 'what-if' analysis." (Jae K Shim & Joel G Siegel, "Budgeting Basics and Beyond", 2008)

"An interactive environment that models a real-world system. Simulations may be conceptual, such as a simulation of genetic inheritance, or operational, such as a flight simulator." ( Ruth C Clark, "Building Expertise: Cognitive Methods for Training and Performance Improvement", 2008)

"A simulation uses a project model that translates the uncertainties specified at a detailed level into their potential impact on objectives that are expressed at the level of the total project. Project simulations use computer models and estimates of risk, usually expressed as a probability distribution of possible costs or durations at a detailed work level, and are typically performed using Monte Carlo analysis." (Cynthia Stackpole, "PMP® Certification All-in-One For Dummies®", 2011)

"An interactive environment in which features in the virtual environment behave similarly to real-world events. Simulations may be conceptual, such as a simulation of genetic inheritance, or operational, such as a flight simulator." (Ruth C Clark & Richard E Mayer, "e-Learning and the Science of Instruction", 2011)

"A process by which processes or models are run repeatedly using a variety of inputs. The outputs are normally captured and analyzed to conduct sensitivity analysis, provide insight around likely potential outcomes, and identify bottlenecks and constraints within existing processes or models." (Evan Stubbs, "Delivering Business Analytics: Practical Guidelines for Best Practice", 2013)

"The practice of building models based on experts’ views on how the parts of a complicated system work." (Brenda L Dietrich et al, "Analytics Across the Enterprise", 2014)

"Developing a model of a complex system and experimenting with the model to observe the results" (Nell Dale & John Lewis, "Computer Science Illuminated" 6th Ed., 2015)

"An analytical technique that models the combined effect of uncertainties to evaluate their potential impact on objectives." (Project Management Institute, "A Guide to the Project Management Body of Knowledge (PMBOK® Guide)", 2017)

"The representation of selected behavioral characteristics of one physical or abstract system by another system." (ISO 2382/1)

Data Science: Mathematical Model (Definitions)

"A mathematical model is any complete and consistent set of mathematical equations which are designed to correspond to some other entity, its prototype. The prototype may be a physical, biological, social, psychological or conceptual entity, perhaps even another mathematical model."  (Rutherford Aris, "Mathematical Modelling", 1978)

"The identification and selection of important descriptor variables to be used within an equation or process that can generate useful predictions." (Glenn J Myatt, "Making Sense of Data: A Practical Guide to Exploratory Data Analysis and Data Mining", 2006)

"Mathematical model is an abstract model that describes a problem, environment, or system using a mathematical language." (Giusseppi Forgionne & Stephen Russell, "Unambiguous Goal Seeking Through Mathematical Modeling", 2008)

"A set of equations, usually ordinary differential equations, the solution of which gives the time course behaviour of a dynamical system." (Peter Wellstead et al, "Systems and Control Theory for Medical Systems Biology", 2009)

"An abstract model that uses mathematical language to describe the behaviour of a system. Mathematical models are used particularly in the natural sciences and engineering disciplines (such as physics, biology, and electrical engineering) but also in the social sciences (such as economics, sociology and political science). It can be defined as the representation of the essential aspects of an existing system (or a system to be constructed) which presents knowledge of that system in usable form." (Roberta Alfieri & Luciano Milanesi, "Multi-Level Data Integration and Data Mining in Systems Biology", Handbook of Research on Systems Biology Applications in Medicine, 2009)

"Mathematical description of a physical system. In the framework of this work mathematical models pursue the descriptions of mechanisms underlying stuttering, putting emphasis in the dynamics of neuronal regions involved in the disorder." (Manuel Prado-Velasco & Carlos Fernández-Peruchena "An Advanced Concept of Altered Auditory Feedback as a Prosthesis-Therapy for Stuttering Founded on a Non-Speech Etiologic Paradigm", 2011)

"Simplified description of a real world system in mathematical terms, e. g., by means of differential equations or other suitable mathematical structures." (Benedetto Piccoli, Andrea Tosin, "Vehicular Traffic: A Review of Continuum Mathematical Models" [Mathematics of Complexity and Dynamical Systems, 2012])

"Stated loosely, models are simplified, idealized and approximate representations of the structure, mechanism and behavior of real-world systems. From the standpoint of set-theoretic model theory, a mathematical model of a target system is specified by a nonempty set - called the model’s domain, endowed with some operations and relations, delineated by suitable axioms and intended empirical interpretation." (Zoltan Domotor, "Mathematical Models in Philosophy of Science" [Mathematics of Complexity and Dynamical Systems, 2012])

"The standard view among most theoretical physicists, engineers and economists is that mathematical models are syntactic (linguistic) items, identified with particular systems of equations or relational statements. From this perspective, the process of solving a designated system of (algebraic, difference, differential, stochastic, etc.) equations of the target system, and interpreting the particular solutions directly in the context of predictions and explanations are primary, while the mathematical structures of associated state and orbit spaces, and quantity algebras – although conceptually important, are secondary." (Zoltan Domotor, "Mathematical Models in Philosophy of Science" [Mathematics of Complexity and Dynamical Systems, 2012])

"They are a set of mathematical equations that explain the behaviour of the system under various operating conditions, and determine the dominant factors that govern the rules of the process. Mathematical modeling is also associated with data collection, data interpretation, parameter estimation, optimization, and provide tools for identifying possible approaches to control and for assessing the potential impact of different intervention measures." (Eldon R Rene et al, "ANNs for Identifying Shock Loads in Continuously Operated Biofilters", 2012)

"An abstract representation of the real-world system using mathematical concepts." (R Sridharan & Vinay V Panicker, "Ant Colony Algorithm for Two Stage Supply Chain", 2014)

"Is a description of a system using mathematical concepts and language. The process of developing a mathematical model is termed mathematical modelling. Mathematical models can take many forms, including but not limited to dynamical systems, statistical models, differential equations, or game theoretic models. A model may help to explain a system and to study the effects of different components, and to make predictions about behaviour." (M T Benmessaoud et al, "Modeling and Simulation of a Stand-Alone Hydrogen Photovoltaic Fuel Cell Hybrid System", 2014)

"A mathematical model is a model built using the language and tools of mathematics. A mathematical model is often constructed with the aim to provide predictions on the future ‘state’ of a phenomenon or a system." (Crescenzio Gallo, "Artificial Neural Networks Tutorial", 2015)

"A mathematical model consists of an equation or a set of equations belonging to a certain class of mathematical models to describe the dynamic behavior of the corresponding system. The parameters involved in this mathematical model are related to a certain mathematical structure. This mathematical model is characterized by its class, its structure and its parameters." (Houda Salhi & Samira Kamoun, "State and Parametric Estimation of Nonlinear Systems Described by Wiener Sate-Space Mathematical Models", 2015)

"Description of a system using mathematical concepts and language." (Tomaž Kramberger, "A Contribution to Better Organized Winter Road Maintenance by Integrating the Model in a Geographic Information System", 2015)

"A description of a system using mathematical concepts and language." (Corrado Falcolini, "Algorithms for Geometrical Models in Borromini's San Carlino alle Quattro Fontane", 2016)

"A mathematical model is a mathematical description (often by means of a function or an equation) of a real-world phenomenon such as the size of a population, the demand for a product, the speed of a falling object, the concentration of a product in a chemical reaction, the life expectancy of a person at birth, or the cost of emission reductions. The purpose of the model is to understand the phenomenon and perhaps to make predictions about future behavior. [...] A mathematical model is never a completely accurate representation of a physical situation - it is an idealization." (James Stewart, "Calculus: Early Transcedentals" 8th Ed., 2016)

"Mathematical representation of a system to describe the behavior of certain variables for an indeterminate time." (Sergio S Juárez-Gutiérrez et al, "Temperature Modeling of a Greenhouse Environment", 2016)

"A mathematical model is a description of a system using mathematical concepts and language. The process of developing a mathematical model is termed mathematical modeling. Mathematical models are used not only in the natural sciences (such as physics, biology, earth science, meteorology) and engineering disciplines (e.g., computer science, artificial intelligence), but also in the social sciences (such as economics, psychology, sociology, and political science); physicists, engineers, statisticians, operations research analysts, and economists use mathematical models most extensively. A model may help to explain a system and to study the effects of different components, and to make predictions about behavior." (Addepalli V N Krishna & M Balamurugan, "Security Mechanisms in Cloud Computing-Based Big Data", 2019)

"A description of a system using mathematical symbols." (José I Gomar-Madriz et al, "An Analysis of the Traveling Speed in the Traveling Hoist Scheduling Problem for Electroplating Processes", 2020)

"An abstract mathematical representation of a process, device, or concept; it uses a number of variables to represent inputs, outputs and internal states, and sets of equations and inequalities to describe their interaction." (Alisher F Narynbaev, "Selection of an Information Source and Methodology for Calculating Solar Resources of the Kyrgyz Republic", 2020)

Systems Engineering: Agent-Based Model/Modeling (Definition)

"Modeling refers to the process of designing a software representation of a real-world system or a small part of it with the purpose of replicating or simulating specific features of the modeled system. In an agent-based model, the model behavior results from behavior of many small software entities called agents. This technique is used to model real-world systems comprised of many decision-making entities with inhomogeneous preferences, knowledge, and decision-making processes. An advantage of this method is that no assumptions need to be made about an aggregate or mean behavior. Instead, this aggregation is made by the model." (E Ebenhoh, "Agent-Based Modelnig with Boundedly Rational Agents", 2007)

"A modeling and simulation approach applied to a complex system or complex adaptive system, in which the model is comprised of a large number of interacting elements (agents)." (Charles M Macal, "Agent Based Modeling and Artificial Life", 2009)

"A modeling technique with a collection of autonomous decision-making agents, each of which assesses its situation individually and makes decisions on the basis of a pre-set of rules. ABM is used to simulate land use land cover change, crowd behavior, transportation analysis and many other fine-scale geographic applications. (May Yuan, "Challenges and Critical Issues for Temporal GIS Research and Technologies", 2009)

"Agent-based models (ABM) are models where (i) there is a multitude of objects that interact with each other and with the environment; (ii) the objects are autonomous, i. e. there is no central, or top-down control over their behavior; and (iii) the outcome of their interaction is numerically computed." (Mauro Gallegati & Mateo G Richiardi, "Agent Based Models in Economics and Complexity", 2009)

"An agent-based model is a simulation made up of a set of agents and an agent interaction environment." (Michael J North & Charles M Macal, "Agent Based Modeling and Computer Languages", 2009)

"Systems composed of individuals who act purposely in making locational/spatial decisions." (Michael Batty, "Cities as Complex Systems: Scaling, Interaction, Networks, Dynamics and Urban Morphologies", 2009)

"A computational model for simulating the actions and interactions of autonomous individuals in a network, with a view to assessing their effects on the system as a whole. (Brian L. Heath & Raymond R Hill, "Agent-Based Modeling: A Historical Perspective and a Review of Validation and Verification Efforts", 2010)

"A model that involves numerous interacting autonomous agents, homogeneous or heterogeneous. The objective of agent-based modeling is to help us to understand effects and impacts of interactions of individuals." (Peter Mikulecký et al, "Possibilities of Ambient Intelligence and Smart Environments in Educational Institutions", 2011)

"a class of computational models for simulating interacting agents." (Enrico Franchi & Agostino Poggi, "Multi-Agent Systems and Social Networks", 2012)

Systems Engineering: System Dynamics (Definitions)

"A field of study that includes a methodology for constructing computer simulation models to achieve better under-standing of social and corporate systems. It draws on organizational studies, behavioral decision theory, and engineering to provide a theoretical and empirical base for structuring the relationships in complex systems." (Virginia Anderson & Lauren Johnson, "Systems Thinking Basics: From Concepts to Casual Loops", 1997) 

"A methodology for studying and managing complex feedback systems, such as one finds in business and other social systems." (Lars O Petersen, "Balancing the Capacity in Health Care", 2008)

"System dynamics is a top-down approach for modelling system changes over time. Key state variables that define the behaviour of the system have to be identified and these are then related to each other through coupled, differential equations." (Peer-Olaf Siebers & Uwe Aickelin, "Introduction to Multi-Agent Simulation", 2008) 

"A continuous simulation of systems exhibiting feedback loops. The feedbacks can either intensify activities of the system (positive feedback) or slow them down and stabilize the system (negative feedback)." (Nikola Vlahovic & Vlatko Ceric, "Multi-Agent Simulation in Organizations: An Overview", 2009)

"Is a scientific tool which embodies principles from biology, ecology, psychology, mathematics, and computer science to model complex and dynamic systems." (Kambiz E Maani, "Systems Thinking and the Internet from Independence to Interdependence", 2009)

"System dynamics is an approach to understanding the behaviour of over time. It deals with internal feedback loops and time delays that affect the behaviour of the entire system. It also helps the decision maker untangle the complexity of the connections between various policy variables by providing a new language and set of tools to describe. Then it does this by modeling the cause and effect relationships among these variables." (Raed M Al-Qirem & Saad G Yaseen, "Modelling a Small Firm in Jordan Using System Dynamics", 2010)

[system dynamics simulation:] "A dynamic form of visualization that combines causal loop diagrams and stock and flow diagrams to create a simulation of the workings of a system from one point in time to another." (DAMA International, "The DAMA Dictionary of Data Management", 2011)

"An approach for capturing the complex inter- and intra- dependencies that characterize systems, including feedback over time." (Howard Passell, "Collaborative, Stakeholder-Driven Resource Modeling and Management", 2011)

This studies the non-linear interaction of systems of many connected equations. The approach is based on differential equations. It describes the dynamical properties of a whole system using internal negative and positive feedback loops as well as the use of stocks and flows. (Martin Neumann, "An Epistemological Gap in Simulation Technologies and the Science of Society", 2011)

"A simulation-modelling approach to understand the structure and behaviour of complex dynamic systems over time." (Jaime A Palma-Mendoza, "Hybrid SD/DES Simulation for Supply Chain Analysis", 2014)

"A systems simulation methodology to study complex dynamic behavior of industrial and social systems based on control engineering and cybernetics." (Michael Mutingi & Charles Mbohwa, 2014)

[system dynamics:] "The interactions of connected and interdependent components, which may cause change over time and give rise to interconnected risks; emerging, unforeseeable issues; and unclear, disproportional cause-and-effect relationships." (Project Management Institute, "Navigating Complexity: A Practice Guide", 2014)

"A continuous simulation of systems exhibiting feedback loops. The feedbacks can either intensify activities of the system (positive feedback) or slow them down and stabilize the system (negative feedback)." (Nikola Vlahovic & Vlatko Ceric, "An Overview of Multi-Agent Simulation in Organizations", 2015)

"System Dynamics is a dynamic modelling approach at system level which is primarily used to understand interconnected systems and their evolution over time. Basic elements to represent the systems are internal feedback loops as well as stocks and flows." (Catalina Spataru et al, "Multi-Scale, Multi-Dimensional Modelling of Future Energy Systems", 2015)

"System dynamics [...] uses models and computer simulations to understand behavior of an entire system, and has been applied to the behavior of large and complex national issues. It portrays the relationships in systems as feedback loops, lags, and other descriptors to explain dynamics, that is, how a system behaves over time. Its quantitative methodology relies on what are called 'stock-and-flow diagrams' that reflect how levels of specific elements accumulate over time and the rate at which they change. Qualitative systems thinking constructs evolved from this quantitative discipline." (Karen L Higgins, "Economic Growth and Sustainability: Systems Thinking for a Complex World", 2015)

"A simulation technique based on the solution of differential equations, in which the status variables of a system vary with continuity." (Lorenzo Damiani et al, "Different Approaches for Studying Interruptible Industrial Processes: Application of Two Different Simulation Techniques", 2016)

"A technique que allow to obtain models to explore possible futures or scenarios and ask 'what if' questions in complex situations." (Ruth R Gallegos, "Using Modeling and Simulation to Learn Mathematics", Handbook of Research on Driving STEM Learning With Educational Technologies, 2017)

"A method through which the dynamic behaviour of a complex system over time can be better understood by taking into account internal feedback and time delays." (Henry Xu & Renae Agrey, "Major Techniques and Current Developments of Supply Chain Process Modelling", 2018)

"Computer-aided methodology able to represent the causal structure of a system through stock-and-flow feedback structures and computer simulations regarding the accumulation of materials, information, people, and money." (Francesca Costanza, "Governing Patients' Mobility to Pursue Public Value: A System Dynamic Approach to Improve Healthcare Performance Management", 2018)

"The basis of system dynamics is to understand how system structures cause system behavior and system events." (Arzu E Şenaras, "A Suggestion for Energy Policy Planning System Dynamics", 2018)