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This paper aims to expose the inadequacy of social marketing to tackle complex social problems, while proposing an expansion in the discipline’ conceptual repertoire. The goal is to incorporate complexity tools, in particular from the system dynamics field, and the promotion of mindware within a true transdisciplinary paradigm.

This paper uses literature review to support the proposed theoretical development. It also presents a short case study.

Most problems that plague our modern societies have a distinctive complex nature that is not amenable to traditional social marketing interventions. Social marketing has simplified the problem of bringing about societal change by thinking that upstream social actors can be influenced in the same way as downstream individuals. This paper shows that this is not the case while proposing a framework to close this gap.

The proposed framework is a theoretical one. It depends on further refinements and actual application to wicked problems.

Complex social problems – or wicked problems – remain widespread in modern societies. Moreover, they are getting worse over time. The paper presents a proposal to redefine the limits of the social marketing discipline so it can be more useful to tackle such problems. Practical approaches such as measuring the success of mindware in the marketplace of ideas are implied in the proposed framework.

The increase in complexity of social problems has not been accompanied by an evolution in the discipline of social marketing. The lack of proper conceptual tools has prevented the discipline from contributing to tackling these problems effectively. Some interventions may actually worsen the underlying problems, as illustrated in the paper.

This paper identifies two major gaps associated with the social marketing discipline, in particular the lack of complexity and systems thinking and the forsaking of ideas (mindware) as a legitimate goal of the discipline. This realization corroborates the claim that boundaries among disciplines are often artificial, hindering the proper understanding of complex social problems. In turn, only the use of adequate conceptual lenses makes it possible to devise interventions and programs that tackle actual causes (instead of symptoms) of complex social problems.

This article explores how complexity theory can help marketers to understand a market and to operate within it. Essentially, it argues that complexity theory has the potential to provide both global and some local explanations of markets and is complementary to local theories like relationship marketing that may be more familiar to marketing managers. It establishes four types of complex systems that might be used to model social systems. Of these four types, complex adaptive systems seem most appropriate to describe markets. This is illustrated in an investigation of Honda in the global automobile industry. Implications for marketing managers centre on the need to understand feedback loops at many levels of a path‐dependent system that are inherently difficult to predict and control.

The purpose of this paper is to propose a new system frame named ACPI for research and development (R&D) management of complex equipments according to the ideas of artificial societies, computational experiments, parallel execution and interactive optimization.

An artificial system which can effectively model, simulate and recur the main features and behaviors of a real R&D system of complex equipment is established at first. The structure and function of the system and its subsystems, and the relation of factors in the system are analyzed. Then one can perform computation experiment, modeling and simulation in the artificial system to obtain the optimal solutions. Finally, practice these solutions in a real system and at the same time perform the solutions in artificial system, forecasting and warning the possible new situations and problems in a realistic process, and provide controlling scheme.

The typical characteristics and solutions of the R&D system of complex equipment are analyzed. The sketch scheme of ACPI, the system frame of ACPI for R&D management of complex equipments are proposed, and the key technologies used in implementing ACPI of R&D system of complex equipment are studied.

The outcome of this paper can be used in computation experiments, management and optimization of R&D systems of complex equipment.

The sketch scheme of ACPI, the system frame of ACPI for R&D management of complex equipments are proposed first. The ACPI system can supply a high‐performance, open and interactive platform for the analog simulation and computation experiments of the R&D process management of complex equipment.

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.

This paper seeks to explore new complex venture approaches needed because the classical twentieth century system engineering model does not accommodate the complexities of twenty‐first century ventures, especially those with significant knowledge management components.

A complexity literature review was performed to identify the attributes of complex ventures. Then the fundamental differences in defining, developing, and implementing complicated traditional systems and complex ventures were explored. The resultant complex venture model builds on the insights derived from chaos and complexity theories; observations of several acquisition successes and failures; and doctoral research on agile enterprise decision support.

Successful traditional systems engineering complicated systems models' built‐in assumptions do not scale to the needed twenty‐first century complex solutions. It is necessary to develop a complex venture model that guides the engineering solutions that: describe complex ventures as flows of intelligence, energy and matter provide value in a dynamic co‐evolving context; provide leadership, not control, with clear and consistent venture‐wide vision that guides empowered individual agent decision making; institute tiered situationally‐aware decision making in both time and place; address factors (material and non‐material) contributing to solution success; provide for rapidly changing context and the co‐evolutionary ventures, including unexpected users, uses, and implementations.

A complex venture conceptual model informs the architecting and systems engineering acquisition practices for this new solution category.

The purpose of this paper is to show that transmission of information and information storage or registration depends on structures. Structures emerge from coordinated sets of constraints. Complex systems depend on their structures to function. The temporal sequence of changes in the levels of the complex system determines its behavior. These three concepts are intimately linked with the environment. Environment, structure, function and behavior form a complex system–environment unit, which is the operational unit of existence for all open complex systems. Therefore, it becomes a point in the directional propagation of the cause, where stimulus environment becomes a Creaon, and then the Creaon becomes a Genon, becoming in turn the response to the experienced environment. The formation of structures is the main phenomenon of evolution. Evolution can also be accepted as free, in the sense that it does not cost additional deaths.

Mathematical and logical development of the structure and thermodynamics in complex systems.

Based on the above considerations, the authors are going to introduce two fundamental principles in Complex systems Theory: the Matthew Effect and the Principle of Sagan.

But as the authors’ purpose is to give a formal definition of a complex system from a totally theoretical point of view, they establish a relationship between concepts of General Systems Theory, Theory of the Environment, linguistics, Information Theory and thermodynamics.

The purpose of this paper is to seek to develop a theoretical framework for understanding the structure of effective knowledge sharing networks in professional organizations.

A literature review is performed to achieve the purpose. This article integrates two streams of literature: related to knowledge network structure and related to professional complex systems, to gain insight into the structure of effective knowledge sharing networks in professional complex systems. This preliminary theoretical framework is then used to put forth strategies for knowledge management and collective learning in professional organizations.

An analysis of knowledge networks and complex systems literatures suggests that effective knowledge sharing networks in complex systems may be richer in density compared to brokerage. However, integrating this analysis with the literature on professional organizations, including “subgoals” theory, suggests that the reverse may be true in professional complex systems, i.e. that effective knowledge sharing networks in professional complex systems may be richer in brokerage and hierarchy, rather than in density.

The paper provides a foundation for future research avenues in the professional organizational context. For instance the framework could be used to explore effective knowledge sharing structures across professional subgroups and hierarchical levels in a hospital context; and across faculty, staff, and administrators in a college/university context.

A key implication is that, in order to enable collective learning in professional organizations, senior executives must make proactive and unceasing efforts to: coordinate knowledge exchange across professional subgroups; create cognitive linkages between subgroup actions and organizational outcomes; and connect professional subgroups with the changing external environment.

The theoretical framework lays a foundation for addressing the gap in the literature related to knowledge creation and collective learning in professional organizations.

The purpose of this research is to systematically review the properties of supply chains demonstrating that they are complex systems, and that the management of supply chains is best achieved by steering rather than controlling these systems toward desired outcomes.

The research study was designed as both exploratory and explanatory. Data were collected from secondary sources using a comprehensive literature review process. In parallel with data collection, data were analyzed and synthesized.

The main finding is the introduction of an inductive framework for steering supply chains from a complex systems perspective by explaining why supply chains have properties of complex systems and how to deal with their complexity while steering them toward desired outcomes. Complexity properties are summarized in four inter-dependent categories: Structural, Dynamic, Behavioral and Decision making, which together enable the assessment of supply chains as complex systems. Furthermore, five mechanisms emerged for dealing with the complexity of supply chains: classification, modeling, measurement, relational analysis and handling.

Recognizing that supply chains are complex systems allows for a better grasp of the effect of positive feedback on change and transformation, and also interactions leading to dynamic equilibria, nonlinearity and the role of inter-organizational learning, as well as emerging capabilities, and existing trade-offs and paradoxical tensions in decision-making. It recognizes changing dynamics and the co-evolution of supply chain phenomena in different scales and contexts.

This paper sets out to report on research by the authors into the development and application of four extensions to Ashby's Law of Requisite Variety (LoRV) that increase its utility in the arena of unplanned changes in hegemonic control of designed complex socio‐technical systems/digital eco‐systems in the built environment that are structurally dynamic or emergent.

Research on which the paper is based focused on exploration of classical systems approaches to the design of complex socio‐technical systems in which ownership, power, control and management of structure and benefit generation and distribution are distributed, dynamic and multi‐constituent. Support for development of these four extensions to Ashby's Law comes from observation of four decades of socio‐technical systems development along with critical thinking that combined systems analysis theories with theories and findings from fields of hegemonic analysis, design research, management, management information systems, behaviour in organisations and sociology.

The paper outlines application of four new extensions to LoRV in relation to unplanned changes in distributions of power, ownership, control, benefit generation and benefit distribution in complex socio‐technical systems/digital eco‐systems in the built environment that are emergent or have changing structures. Three of these extensions have been outlined earlier in relation to the design of learning object‐based e‐learning systems. The fourth extension builds on these via application of Coasian analysis. The paper also describes a suite of five guidelines to assist with the design of complex socio‐technical systems derived from the four extensions to Ashby.

The four extensions of Ashby's Law that underpin the design guidelines in this paper are deduced from observation and critical analysis rather than being “proven” empirically. They are derived from observation of the behaviour of real‐world complex systems together with critical analytical thinking that integrated theory and research findings from a range of disciplines where each informs understanding of hegemonic aspects of emergent complex socio‐technical systems involving multiple, changing constituencies, and evolving system structures.

A design method is derived comprising five design guidelines for use in pre‐design and design of complex socio‐technical systems/digital eco‐systems in the built environment.

The paper describes the application of four new extensions to LoRV that extend the analytical role of Ashby's Law in diagnosis of changes in power relations and unintended design outcomes from changes in the generation and control of variety in complex, multi‐layered and hierarchical socio‐technical systems that have multiple stakeholders and constituencies. From these, a suite of five new design guidelines is proposed.

As the engineering design process becomes increasingly complex, multidisciplinary teams need to work together, integrating diverse expertise across a range of disciplinary models. Where changes arise, these design teams often find it difficult to handle these design changes due to the complexity and interdependencies inherent in engineering systems. This paper aims to develop an innovative approach to clarifying system interdependencies and predicting the design change propagation at the asset level in complex engineering systems based on the digital-twin-driven design structure matrix (DSM).

The paper first defines the digital-twin-driven DSM in terms of elements and interdependencies, where the authors have defined three types of interdependency, namely, geospatial, physical and logical, at the asset level. The digital twin model was then used to generate the large-scale DSMs of complex engineering systems. The cluster analysis was further conducted based on the improved Idicula–Gutierrez–Thebeau algorithm (IGTA-Plus) to decompose such DSMs into modules for the convenience and efficiency of predicting design change propagation. Finally, a design change propagation prediction method based on the digital-twin-driven DSM has been developed by integrating the change prediction method (CPM), a load-capacity model and fuzzy linguistics. A section of an infrastructure mega-project in London was selected as a case study to illustrate and validate the developed approach.

The digital-twin-driven DSM has been formally defined by the spatial algebra and Industry Foundation Classes (IFC) schema. Based on the definitions, an innovative approach has been further developed to (1) automatically generate a digital-twin-driven DSM through the use of IFC files, (2) to decompose these large-scale DSMs into modules through the use of IGTA-Plus and (3) predict the design change propagation by integrating a digital-twin-driven DSM, CPM, a load-capacity model and fuzzy linguistics. From the case study, the results showed that the developed approach can help designers to predict and manage design changes quantitatively and conveniently.

This research contributes to a new perspective of the DSM and digital twin for design change management and can be beneficial to assist designers in making reasonable decisions when changing the designs of complex engineering systems.