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The purpose of this paper is to present research in the area of the modeling of complex systems using feed‐forward neural network.

Applications of multilayer neural networks with supervisor learning on the own simulator program wrote in Borland^® Pascal Language. Series‐parallel identification method is applied. Tapped delay lines (TDL) in static neural networks for modeling of dynamic plants are used. Gradient and heuristic learning algorithms are applied. Three kinds of calibration of learning and testing data are used.

This paper illustrates that feed‐forward multilayer neural networks can model complex systems. Feed‐forward multilayer neural networks with TDL can be used to build global dynamic models of complex systems. It is possible to compare the quality both models.

The learning and testing data from real systems to tune neuronal models require use of calibrating these data to range 0‐1.

The models quality depends on kind of calibration learning data from real system and depends on kind of learning algorithms.

The method and the learning algorithms discussed in the paper can be used to create global models of complex systems. The multilayer neural network with TDL can be used to model complex dynamic systems with low dynamics.

Current motivation theory and research face serious theoretical, methodological, and practical challenges. One central challenge is the fact that research has focused mainly on motivation for traditional achievement tasks such as graded assignments and normative educational trajectories. Arguably, current motivation theory and research may be inadequate to characterize adaptive motivation in the uncertain, changing, and unpredictable environments of the twenty-first century. How might motivation researchers conceptualize students’ motivation in such dynamic and complex contexts? How can motivational research inform educators, administrators, and policymakers in designing curricula, pedagogy, and evaluation and accountability systems to prepare students for such a world? In the current chapter, we address these challenges with a perspective on motivation as a complex dynamic system (CDS) that is based in the person’s identity. We begin with a brief review of the challenges to the current prevalent approach to motivation research, highlighting the need for a new paradigm. We then review assumptions of the CDSs approach that render it useful for understanding motivation in continuously changing and unpredictable environments. We then present a CDS conceptual model of identity and motivation that incorporates constructs and processes from a variety of identity and motivational theories – the Dynamic Systems Model of Role Identity (DSMRI). We follow with a conceptualization of the characteristics of the identity-motivation system most adaptive for growth in changing and unpredictable environments. We end by considering the implications of this perspective for motivational theory and research and for educational practice and policy.

This chapter aims to identify the main determinants that define the architectural properties of network emergence and significantly influence the dynamics underlying network evolution in time. The identification and analysis of these determinants, as well as the dynamic processes tied to them, allows to appreciate the competitive bases and consequences of network morphology. To this purpose, using a complex systems perspective as an integrative conceptual approach, we represent networks as complex dynamic systems of knowledge and capabilities. We perform a comparative in-depth analysis of the processes underlying the emergence and evolution of STMicroelectronic's global network and of Toyota's supplier network in the US so as to allow an elucidatory empirical assessment of the theoretical representation elaborated in the article.

Dynamic planning and scheduling forms a widely adopted smart strategy for solving real-world problems in diverse business systems. This paper uses deductive content analysis to explore secondary data from previous studies in dynamic planning and scheduling to draw conclusions on its current status, forward action and research needs in construction management.

The authors searched academic databases using planning and scheduling keywords without a periodic setting. This research collected secondary data from the database to draw an objective comparison of categories and conclusions about how the data relates to planning and scheduling to avoid the subjective responses from questionnaires and interviews. Then, applying inclusion and exclusion criteria, we selected one hundred and four articles. Finally, the study used a seven-step deductive content analysis to develop the categorisation matrix and sub-themes for describing the dynamic planning and scheduling categories. The authors used deductive analysis because of the secondary data and categories comparison. Using the event types represented in a quadrant mapping, authors delve into where, when, application and benefits of the classes.

The content analysis showed that all the accounts and descriptions of dynamic planning and scheduling are identifiable in an extensive research database. The content analysis reveals the need for multi-hybrid (4D BIM-Agent based-discrete event-discrete rate-system dynamics) simulation modelling and optimisation method for proffering solutions to scheduling and planning problems, its current status, tools and obstacles.

This research reveals the deductive content analysis talent in construction research. It also draws direction, focuses and raises a question on dynamic planning and scheduling research concerning the five-integrated model, an opportunity for their integration, models combined attributes and insight into its solution viability in construction.

This chapter introduces readers to a complex adaptive systems approach for integrating research on genes, behavior, and social structures/institutions. Until recently, scientists have resorted to reductionism as a decoding and epistemological strategy for understanding human health. The complex bonds among health’s biological, behavioral, and social dimensions, however, cannot be fully grasped with reductionist schemas. Moreover, because reducing and simplifying can lead to incomplete understanding of phenomena, the resulting deficient knowledge has the potential to be harmful.

To achieve its purpose, this primer will: (1) introduce fundamental notions from complexity science, useful for inquiry and practice integrating research on genes, behavior, and social structures; (2) outline selected methodological strategies employed in studying complex adaptive/dynamic systems; (3) address the question, “Specifically, how can a dynamic systems approach be helpful for integrating research on genes, behavior, and social structures/institutions, to improve the public’s health?”; and (4) provide examples of studies currently deploying a complexity perspective.

The originality/value of this primer rests in its critique of the research status quo and the proposition of an alternative lens for integrating genomic, biomedical, and sociological research to improve the public’s health. The topic of complex adaptive/dynamic systems has begun to flourish within sociology, medicine, and public health, but many researchers lack exposure to the topic’s basic notions and applications.

The paper aims to investigate what is the best ontological framework of anticipatory systems. Its aim is to argue the thesis that the ontology on which anticipatory systems are based should be a dynamic one: a kind of process ontology. It seeks to include a demonstration of the fruitfulness of such an ontological framework for the investigation of anticipatory systems.

The methodology of the paper is a process ontological one. The objectives are achieved by a comparative analysis of the static and dynamic approaches to the ontological framework.

A process ontological framework is a reliable basis for the substantiation of the thesis that there is no great gap between living and non‐living systems as far as anticipation is concerned.

An example is represented of an anticipatory non‐living system that is artificially created and is programmed as a self‐control system. In this respect the paper has some practical implications.

A new approach is suggested to the investigation of anticipatory systems. It could be of interest not only for philosophers, but also for scientists who work on ontology as technology.

This research presents an ontological model, to communicate the impact of dynamic preconditions for peri-urban communities. As such, this paper approaches perturbation communities as social-complex-adaptive-systems.

Previous assessment of dynamic preconditions have typically been based on top-down approaches. Through the lens of social-complex-adaptive and systemic design approaches (requiring a range of different disciplines), this work focuses on providing a broader view towards periurban research. The methodological approach involved academic literature, fieldwork observations, in-depth discussions with community, government, experts and research groups, focusing on a region called “Xochimilco” on the outskirts of Mexico City, a unique pre-Hispanic, Aztec ecosystem. This evolving man made agricultural/ecological structure of island plots, still provides environmental services to Mexico City. This region provides the basis of the research and subsequent ontological model. Ontology, in this instance, refers to the nature of being within a range of constraining dynamic forces relating to resilient behaviors of the current Xochimilco perturbation ecosystem.

Xochimilco can be considered as a longitudinal phenomenon that contributed to the understanding of observable resilient and precondition elements between the past and present of a living complex-adaptive-system.

The research has provided a better understanding of community resilience through preconditions, contributing towards preparation of environmental change and future urbanization. To this end, the research focused on visualizing key dynamics elements for communities attempting to absorb new urban conditions (being continuously pushed into it).

The outcomes of this research have provided specific systemic, bottom up approaches with ontological modeling to assist with visualizing and understanding intangible dynamic conditions that impact high complex areas of perturbation regions.

Although researchers have demonstrated a keen interest in knowledge collaboration in online encyclopedias, previous studies have seldom explored the dynamic interrelations in online encyclopedias over time that involve the iteratively melding of individual cognitive system and knowledge collaboration system. Therefore, this paper aims to reveal the structure and dynamics of knowledge collaboration in online encyclopedias from a perspective of system dynamics (SD).

This paper proposes a general activity system of knowledge collaboration in online encyclopedias based on Engeström’s activity theory. According to the SD methodology proposed by Forrester, this study develops a holistic SD model by identifying interactions of knowledge collaboration factors based on behavioral theories; validating the SD model by structural tests and behavior tests involving historical data of English Wikipedia; and conducting simulation to capture the interactive dynamics of the salient factors of knowledge collaboration.

According to the SD methodology, this study develops and validates an SD model to explore interesting dynamic interrelations among core factors (contributors, conflicts, discussions, entries quantity and entries quality) that are neglected by previous research. The results show that there is a significant negative feedback relationship between inactive contributors and entries quality, between contributors and conflicts and between edit conflicts and entries quality. There is a complicated nonlinear feedback relationship between active contributors and entries quality, and between edit conflicts and discussions.

Different from prior empirical studies that normally investigate the unidirectional linear relationships among prominent factors of knowledge collaboration in online encyclopedias from a static perspective, this study captures a dynamic picture of their interrelations by unfolding their behavior patterns over time. The main contribution of this study is to develop a holistic SD model and to reveal and elaborate on the complex dynamics involved online encyclopedias based on activity theory.

Complexity researchers have identified four basic assumptions underlying non‐linear dynamic systems (NDS): the assumption that change is a constant; the assumption that emergent systems are not reducible to their parts; the assumption of mutual dependence; and the assumption that complex systems behave in non‐proportional ways. In this paper I use these new assumptions as a basis for explaining why order emerges in organizations, and for uncovering a three‐stage process model of complex adaptive systems change (CASC). The insights from these NDS models are revealed through examples from two entrepreneurial firms undergoing transformative shifts in their development. These assumptions of NDS and the model of CASC may therefore be useful for understanding order creation and self‐organizing processes in work groups, project ventures, and organizations.