A computational literature review of the field of System Dynamics from 1974 to 2017

Kunc, Martin; Mortenson, Michael J.; Vidgen, Richard

doi:10.1080/17477778.2018.1468950

Cited by 50 publications

(33 citation statements)

References 21 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Currently, SDMs are used to support policy design and management for sustainable development in those fields characterized by a high level of uncertainty, such as transport management [58,61,62], land use [63], waste management [59] and also sustainable urban development [13,57,64,65]. In the last few decades, an increase of SDM application has been observed in literature [66], especially in the urban development field.…”

Section: Methodological Background and State Of Artmentioning

confidence: 99%

Dynamic Models for Exploring the Resilience in Territorial Scenarios

et al. 2019

View full text Add to dashboard Cite

The present paper focuses on the role covered by dynamic models as support for the decision-making process in the evaluation of policies and actions for increasing the resilience of cities and territories. In recent decades, urban resilience has been recognized as a dynamic and multidimensional phenomenon that characterizes urban and metropolitan area dynamics. Therefore, it may be considered a fundamental aspect of urban and territorial planning. The employment of quantitative methods, such as dynamic models, is useful for the prediction of the dynamic behavior of territories and of their resilience. The present work discusses the system dynamics model and the Lotka-Volterra cooperative systems and shows how these models can aid technicians in resilience assessment and also decision makers in the definition of policies and actions, especially if integrated in wide evaluation frameworks for urban resilience achievements. This paper aims to provide an epistemological perspective of the application of dynamic models in resilience assessment, underlying the possible contribution to this issue through the analysis of a real case study and methodological framework. The main objective of this work is to lay the basis for future compared applications of these two models to the same case study.in order to underline dynamic model (DM) features that are closely related to the decision-making process in territorial and urban planning [11]. DMs are recognized as suitable tools to evaluate policies and actions aimed at increasing urban resilience [12][13][14]. This property is related to the fact that these models are built and grounded on dimensions related to both urban systems and urban resilience. In fact, during the construction of the model, it is necessary to select and identify which aspects of a territorial system have to be included with reference to the evaluation goal, from the environmental to the economic dimension [11,12,15]. In this sense, DMs are able to reveal both the dynamic behavior of urban and territorial systems and the impacts of policies on the key variables identified.Specifically, the main aim of this work is to study the principal characteristics of the system dynamics model (SDM) and Lotka-Volterra models (LV) in order to apply both to the same case study. The final purpose of this investigation is to understand the importance of the modelling approach in the field of resilience evaluation.The paper is structured as follows: Section 2 describes the current state of the art of resilience and urban resilience; Section 3 explains the role of dynamic models-the system dynamic models (SDM) and Lotka-Volterra models (LV)-in the decision-making process and summarizes their methodological background, state of art and some illustrative examples; Section 4 explains how the SDM models and LV models could contribute to urban planning; a comparative matrix is developed to investigate the utility of the considered models in predicting support in the design of future transformation scenarios; and Section 5 incl...

show abstract

Section: Methodological Background and State Of Artmentioning

confidence: 99%

Dynamic Models for Exploring the Resilience in Territorial Scenarios

et al. 2019

View full text Add to dashboard Cite

show abstract

“…The mixture of words in a document constitutes a set of latent topics. NLP automatically analyses the occurrence and hidden relationships of these words in the documents to define each topic as a probability distribution (Cunningham & Kwakkel 2016, Jiang et al 2016, Tong & Zhang 2016, Kunc et al 2018, Agrawal et al 2018.…”

Section: Natural Language Processingmentioning

confidence: 99%

A Bibliometric Method for Analysis of Systems Engineering Research

Oosthuizen

Pretorius

2020

INCOSE International Symp

View full text Add to dashboard Cite

Since systems engineering is still a relatively young and growing discipline, it requires a periodic analysis, taking into account past research to derive the requirements for future growth. Published research provides a good indication on the progress and maturity of a scientific discipline. Bibliometric analysis is a valuable tool to assess published research. This paper establishes a method to determine the main research topics published in the Systems Engineering Journal (INCOSE) since its inception. The research and associated analysis method applies Natural Language Processing with Topic Modelling to extract the main topics from the abstracts of all the papers published in the journal. The analyzed data provides the trends in topic coverage over time.

show abstract

“…It combines qualitative research and quantitative analysis to simulate the system influence mechanism and operation mode. System dynamics is effective for studying complex system characteristics and time-varying dynamic user behavior [20]. Moreover, system dynamics often uses graphical representations to investigate the feedback loop structure of the system.…”

Section: System Dynamicsmentioning

confidence: 99%

“…Therefore, auxiliary variables in the user behavior subsystem can be calculated as shown in Equations (11) Patents have a five-month time lag. Therefore, rate variables in the governance mechanism subsystem can be calculated as shown in Equations (17)- (20).…”

Section: (3) Auxiliary Variablesmentioning

confidence: 99%

Impact of Open Innovation Communities on Enterprise Innovation Performance: A System Dynamics Perspective

Gong

2019

Sustainability

View full text Add to dashboard Cite

Open innovation communities (OICs) can help enterprises make full use of external knowledge resources from users, but problems such as low user participation and low conversion rate of creative ideas impact the efficiency of OICs. Most studies on this topic employ qualitative or empirical methods from a static perspective, but ignore the effect of interaction between enterprises and users as well as the cumulative effect of time. A discussion on the dynamic evolution process of open innovation is lacking. Based on a review of the literature on OICs, innovation performance, and system dynamics, this study proposes a conceptual model of innovation performance impact, which comprises the knowledge management, governance mechanism, and user behavior subsystems. Xiaomi’s OIC in China was selected as the research object, and relevant data were collected through a web spider. According to the system dynamics modeling method, a causal relationship analysis was carried out on the three aforementioned interrelated subsystems. Then, a stock flow chart with 32 variables was constructed to determine the initial values and calculation equations for each variable. Finally, the model was constructed and verified using Vensim PLE software. The simulation results were as follows. (1) The number of product releases in the Xiaomi OIC was positively correlated with the number of posts, comments, and views. Compared with user interaction behavior (i.e., commenting and viewing), the impact of user innovation behavior (i.e., posting) on enterprise innovation performance (i.e., number of patents) is clearer. Specifically, regarding interaction behavior, the impact of the users’ commenting behavior on innovation performance (i.e., number of product releases) was relatively clearer than that of their viewing behavior. (2) Governance mechanism (i.e., R&D investment and management expense), which comprises technical and organizational mechanisms, positively affected the innovation performance of enterprises. Compared with the organizational mechanism (i.e., management expense), the impact of the technical mechanism (i.e., R&D investment) on the innovation performance was clearer. (3) Governance mechanism helped to increase the number of users in the OIC, and, in turn, affected the user innovation and interaction behavior. (4) The technical mechanism positively affected knowledge application capability, which, in turn, had a positive impact on the innovation performance of enterprises. Based on these findings, management strategies are proposed for the establishment and development of OICs.

show abstract

A computational literature review of the field of System Dynamics from 1974 to 2017

Cited by 50 publications

References 21 publications

Dynamic Models for Exploring the Resilience in Territorial Scenarios

Dynamic Models for Exploring the Resilience in Territorial Scenarios

A Bibliometric Method for Analysis of Systems Engineering Research

Impact of Open Innovation Communities on Enterprise Innovation Performance: A System Dynamics Perspective

Contact Info

Product

Resources

About