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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.

This chapter uses the theory of complex systems as a conceptual lens through which to compare the work of Friedrich Hayek with that of Vincent and Elinor Ostrom. It is well known that, from the 1950s onwards, Hayek conceptualised the market as a complex adaptive system. It is argued in this chapter that, while the Ostroms began explicitly to describe polycentric systems as a class of complex adaptive system from the mid-to-late 1990s onwards, they had in fact developed an account of polycentricity as displaying most if not all of the hallmarks of organised complexity long before that time. The Ostromian and Hayekian approaches can thus be seen to share a good deal in common, with both portraying important aspects of society – the market economy in the case of Hayek, and public economies, legal and political systems, and environment resources in the case of the Ostroms – as complex rather than simple systems. Aside from helping to bring out this aspect of the Ostroms’ work, using the theory of complex systems as a framework for comparing the Hayekian and Ostromian approaches serves two other purposes. First, it can be used to show how one widely criticised aspect of Hayek’s theory of society as a complex system, namely his account of cultural evolution via group selection, can be strengthened by an appeal to the work of Elinor Ostrom. Second, it also helps to resolve a tension – ultimately acknowledged by the Ostroms themselves – between some of their explicit methodological pronouncements and the actual, substantive approach they adopted in their analysis of polycentric systems.

In this chapter, we raise awareness of the larger network in which multiteam systems (MTSs) are situated. We posit that in the complex operations conducted by military units, MTSs are not isolated entities, but rather exist in exponentially complex systems that include additional challenges for both research and practice.

An operational example involving an Army Brigade Combat Team Headquarters is presented to explain the details of the exponentially complex MTSs inherent in military operations, raise awareness about challenges that plague successful mission accomplishment, and discuss the way forward for research and practice.

The Army Brigade Combat Team Headquarters is characterized as a traditional MTS, embedded in a system of hierarchical MTSs, further embedded within a parallel structure of MTSs. Challenges inherent in these organizational structures provide direction for research and practice to address the exponentially complex meta-systems that are prevalent throughout the military.

While researchers have begun to address teams existing in larger networks, or MTSs (Mathieu, Marks, & Zaccaro, 2001), much of the existing research is based on small or isolated systems. As a result, our understanding of the meta-systems in which many of these MTSs exist is limited. This chapter provides concrete examples of an exponentially complex MTS within a military environment and highlights challenges to be addressed in both research and practice.

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.