Showing papers in "Emergence: Complexity and Organization in 2009"

Temporality in Link Prediction: Understanding Social Complexity

Abstract: This article describes research into the discovery and modelling of emergent temporal phenomena in social networks. It summarizes experimental results that bring together two views in contemporary science: Bayesian analysis and link prediction, to enhance the current understanding of emergent temporal patterns in social network analysis (SNA), particularly in value creation through social connectedness-an important, and growing, discipline within management science. Traditional link prediction methods use the values of metrics in a graph to determine where new links are likely to arise, and little work has been done on analyzing long-term graph trends. We have found that existing graph generation models are unrealistic in their prediction, and can be complemented through the use of temporal metrics, in the study of some networks. To date, no temporal information has been used in link prediction research, thereby excluding valuable temporal trends that emerge in sociogram sequences and also lowering the accuracy of the link prediction. We extracted information from the Pussokram online dating network dataset, and 9,939 cases of each class were formed. Logistic regression in the Weka data mining system was used to perform link prediction. Our results show that temporal metrics are an extremely valuable new contribution to link prediction, and should be used in future applications. In addition to using metrics to measure the local behaviors of participants in social networks, we used Bayesian networks to model the interrelationships between the metrics as local behaviors and links forming between individuals as emergent behaviors (social complexity). We also explored how the metrics evolve over time using Dynamic Bayesian Networks (DBN). (ProQuest: ... denotes formulae omitted.) Introduction Social networks are complex systems that are characterized by high numbers of interconnected component entities, and a high degree of interaction between these entities. The interrelationships in such a network are dynamic and evolve over time. Temporal changes in social networks are difficult to understand and anticipate. The interrelationships between the component entities in a social network and its global behavior can be so numerous and mostly hidden, and can affect so many different entities throughout the social network that it becomes extremely difficult to comprehend. Complexity theory is ideally suited to study social networks. Complex adaptive systems theory is a branch of complexity theory that studies systems that consist of agents that are collectively able to evolve in response to environmental changes. The agents in such a system constantly act and react to the actions of other agents and events in the environment. A social network is a complex adaptive system, in which people are agents interacting with each other. In order to understand social complexity, the local behaviors ofthe participants must be understood, as well as how they act together and interact with the environment to form the whole. To model this, we use Bayesian network techniques to mine and model emergent relationships between local behaviors and global behaviors in social networks over time. Social Complexity The complex structure of any social organization can be thought of as a network of individuals/agents (Nohria 8c Eccles, 1992: 288; Lincoln, 1982), sometimes termed network actors, that operates and is operated on in an environment which itself is an environment of other distributed organizations (Van Wijk et al, 2003; Potgieter et al, 2006), and the actions of agents within the network are shaped and constrained because of their position and embeddedness in the network (Nohria, 1992). Complex adaptive systems theory, a branch of complexity theory that is well suited to study social networks, investigates systems that consist of agents that are collectively able to evolve in response to environmental changes (i. …

Derivation of Factors Facilitating Organizational Emergence Based On Complex Adaptive Systems and Social Autopoiesis Theories

Abstract: Modern turbulent business environments are characterized by rapid change that make businesses unpredictable, which brings emergence to the core of modern organizations. Deriving factors facilitating organizational emergence has been undertaken by drawing on complex adaptive systems (CAS) and social autopoiesis theories. Social autopoiesis was particularly chosen as it focuses on social elements, such as communication, morale, trust, etc. and their relation to social emergence, whereas CAS theory concentrates more on adaptive mechanisms that make a CAS produce emergent order, such as inter-relations, interactions, edge of chaos, feedback, etc. This led to the identification of various factors facilitating emergence and the development of a framework for utilizing these factors that were organized into two dimensions. First the factors are classified as either tangible or intangible. Second, the factors are classified as either dynamic, i.e., realize emergent properties, or they are concerned with the enabling infrastructure, i.e., enable the dynamic factors to become effective, or they are controlling factors, i.e., they attempt to balance excessive change with stability to prevent descent into chaos. The framework was applied to an Information Systems Development (ISD) project which showed that it is applicable to any type of business sector. This framework is argued to be a step forward to realize organizational emergence based on complexity principles derived from literature. The split between factors facilitating emergence and generic principles of CAS is not clear in the complexity literature and it is argued to be an important contribution of the paper. Introduction In turbulent business environments organizations need to react quickly and creatively to make the most of new opportunities and business models. These new imperatives of business practice require organizations to selforganise and become more flexible to handle change (Goldman et al, 1995). Of key importance to organizations in responding successfully to change is the concept of emergence. Complexity science, it has been argued, is a way of addressing and improving such capabilities in organizations, as it is concerned with the role of chance, emergence and contingency in the face of frequent and continuous change (Montuori, 2003). McKelvey (1997), Stacey et al (2000) and Mitleton- Kelly (2003) illustrate the growing interest in understanding organizations and new management practices in terms of theories of complexity that seek to provide new ways of thinking and reasoning in relation to emergent behavior. In this paper factors facilitating organizational emergence have been identified by interpreting complex adaptive systems (CAS) and social autopoiesis theories with the aim of identifying mechanisms or strategies that raise the emergent properties of social business enterprises. Social autopoiesis was chosen as it focuses on social elements of emergence, such as communication, collaboration, morale, trust, etc., whereas CAS theory concentrates more on adaptive mechanisms that make a CAS produce emergent order, such as inter-relations, interconnectivity, edge of chaos, feedback, etc. A thorough literature review of managementrelated contributions in the field of complexity and social autopoiesis theories was undertaken to extract mechanisms or strategies that are argued will facilitate the emergence of new work arrangements in the face of frequent change. Based on this a framework has been derived that summarizes the so-called factors that facilitate organizational emergence. The framework classifies factors as tangible and intangible, and it differentiates between dynamics, enabling infrastructure and controls, amongst emergence factors. Preliminary validation of the framework was carried out through its empirical application in the context of an information systems development (ISD) project, a business to business e-commerce portal. …

Social and Ecological Transitions: Winemaking in California

Abstract: With rising interest in sustainability, ecology is an increasingly important dimension of organizational research. Yet few empirical studies integrate local ecology into coevolutionary approaches where firms are key actors, and fewer still approach the question of sustainability and organizations from a systems perspective. In this paper, we ask how organizations can effectively participate in efforts to increase sustainability from a systems perspective. We develop an interdisciplinary framework for understanding firm-ecology relationships and then explore how this framework sheds light on regional planning and industrial practice in northern California's wine industry. Introduction Porter (2006) argues that we need to take a systems perspective to adequately describe how firms affect natural systems and how they can contribute to increased sustainability. This is a new area for research. While studies on organizations and the natural environment have grown considerably over the last few decades (Bansal 8c Gao, 2006; Jermier et al, 2006), only a few use systems theories (e.g., Allen, 1997; Boons, 2008; Loorbach et al, forthcoming; Whiteman et al, 2004). Instead, organizational research tends to focus on whether or not it pays to be green (Berchicci 8c King, 2007), on the various strategic approaches to framing environmental issues (Etzion, 2007; Jermier et al, 2006), on the impact of regulation on firm behavior, and on measuring the environmental performances of organizations in terms of waste production, resource use, or the adoption of ISO 14001 (Bansal 8c Gao, 2006). But persistent sustainability problems - such as climate change or unsustainable agriculture - require more radical and structural changes within and between organizations (Rotmans, 2005) as they relate to the needs and constraints ofthe local ecology (Allen, 1997; Boons, 2008; Guthey, 2004; Lockwood, 2007; Whiteman et al, 2004). From a complex systems perspective, sustainability and resilience emerges from the coevolution of social, ecological and economic systems (Allen, 1997; Porter, 2006; Korhonen 8cSeager, 2008). As Seager (2008: 447) writes, "[t]he locus of study in sustainability science is on the interaction between human and natural systems" (italics in original). Capturing this interaction requires a multidisciplinary approach to organizational research which moves beyond a linear search for eco-efficiencies (Allen, 1997; Korhonen 8c Seager, 2008; Loorbach et al, forthcoming). In this paper, we look to the fields of ecology and geography for theoretical tools that can help managers and researchers think about complex organization-nature relationships. We also empirically examine how organizations can effectively participate in efforts to increase sustainability from a systems perspective given the needs and constraints of a specific local environment. In section one, we briefly review the growing body of work in organizations and ecology. Section two identifies how approaches from ecology can help clarify organizationnature relationships (e.g., Holling, 1986; Folke et al, 2002). In section three, we draw upon research from economic geography to propose that organizations can usefully be viewed as powerful systemic actors that coproduce social understandings of "place" that has direct implications for sustainability. We then present empirical findings in section four to illustrate how organizations (including firms) have coevolved with a specific place through a case study of winemaking and regional planning in northern California. Data for the case study was collected using documentary analysis, interviews, and observation in order to show how firms embracing environmental values can generate innovations in production processes and markets (Guthey, 2004). In Napa and Sonoma County, firms have contributed to an emergent set of policies concerning ecological protection, agricultural land preservation and promotion for more than 40 years with the result that firms and other stakeholders seem to be coproducing not just wine but also the 'place' associated with its production. …

The Complexity of Complexity Theory: An Innovative Analysis

Abstract: As more scholars join the conversation around complexity theory (CT), it seems a useful time to ask ourselves if we are talking about the "same thing?" This concern is highlighted by the present survey, which finds more conflict than agreement between definitions. In contrast to the conflict, a path toward common ground may be found by applying the idea of a "robust" theory. A robust theory is expected to be more effective in application and more reasonably falsifiable. In this paper, Reflexive Dimensional Analysis (RDA) is used to analyze existing definitions of CT. These definitions are deconstructed, redefined as scalar dimensions, combined, and investigated to identify co-causal relationships. The robustness of CT is identified as 0.56 on a scale of zero to one. Paths for advancing the theory are suggested, with important implications for complexity science. Introduction: Seeking the Core of Complexity Theory Given the breadth, depth, and growth of the current conversation, it seems reasonable to ask - exactly what is this thing called "complexity theory?" For although there are many definitions of CT, it has been suggested, that there is no unified description (Axelrod 8c Cohen, 2000: 15; Lissack, 1999: 112). While this plurality may reflect the many voices engaged in the conversation, it also calls into question the validity of the theory because there is no common sense as to what the theory "is." Indeed, the general assumption seems to be that we are all talking about the "same thing." Like blind men discussing an elephant, such assumptions may lead to false conclusions and unnecessary conflict. While the academic process thrives on the differences between points of view, the extent of those differences calls into question whether scholars are, indeed, talking about the same thing. After all, if one author states that CT may be understood through concepts "A, B, and C" while another author states that the relevant concepts are "C, E, and F," there is some conceptual overlap, but there are also inherent contradictions. Although according to their authors, these descriptions fit under the general rubric of CT, these differences may be seen as representing a conflict in the common understanding of CT, and so reflect differences in our understanding of systems from atoms to institutions. The issue of understanding of a body of theory has been of concern for decades. In one attempt to make sense of the issue, theories are described as having of a "hard core" of unchanging assumptions, surrounded by a more changeable "protective belt" (Lakatos, 1970). When a theory is challenged, a theorist may rise to defend it with a new concept that changes the belt, but presumably leaves the core intact. In the present paper, I seek to identify the core of CT. This effort will provide general and specific support for the continued development of CT. If the core is defined as "that which is generally accepted," it might be easy to define the core of CT. Unfortunately; no such commonality seems to exist (as will be explored in greater depth below). Some other indicator is then needed for the core. Where the social sciences might be generally said to have highly variable protective belts of theory, it should be noted that Ohm's I=E/R is a robust theory. I use the term robust in the same way that it is used in physics and mathematics, to describe a theory where each dimension of the theory may be determined by the other dimensions (this will be discussed in greater detail below). In the present article, I will identify how an understanding of CT might be shifted from the shifting obfuscacion of Lakatos's outer belt, toward an enduring and useful law. When our theories attain this level of advancement, we may anticipate meaningful changes in the way we study institutions. Leaving that lack of effective theory unquestioned is like ignoring our fundamental assumptions. And, as Lichtenstein (2000a: 539) suggests, ". …

From traditional knowledge management in hierarchical organizations to a network centric paradigm for a changing world

Abstract: At the beginning of the 21st century, all organizations need to address the continually changing social and economic landscape in which they operate. In this landscape organizations need to be responsive, flexible and agile and acquire the capability to leverage information and use collective knowledge to make appropriate decisions quickly and effectively. The practice of knowledge management allows knowledge workers to participate in dynamic processes that generate and use collective knowledge. However the complexity that arises from a continually changing global environment highlights the need for knowledge management to move in new directions both in practice and theory. This paper proposes approaches to knowledge management that incorporate concepts from complexity theory leading to the adoption of a network-centric paradigm in organizations, complementing or replacing traditional hierarchical bureaucracies. Introduction At the beginning ofthe 21st century, all organizations, whether government, industrial, commercial or civil, need to address the continually changing social and economic landscape, in which they operate. A central theme of this change is the emergence of information and knowledge as major elements of wealth creation processes including the growth of intellectual and social capital (Sveiby, 1997). The current corporate interest in knowledge is based on a realization that emerging economic theories, coupled with social and industrial restructuring, demand a more rigorous approach to the exploitation of knowledge, and knowledge making capabilities, as organizational resources (Drucker, 1998). As knowledge workers, modern employees are participating in dynamic processes that generate and use collective knowledge in a changing organizational landscape (Ivari & Linger, 1 999; Pfaff & Hasan, 2007). The practice of knowledge management (KM) is now distinguished from information systems and information management (Hart & Warne, 2005; HB189, 2004). As encapsulated in the Australian Standard (AS 5037, 2005), KM manifests itself in organizations through a variety of interpretations and implementations depending on the enterprise, the pressures for innovation and the market context. KM must contend with the increasing complexity that comes with the continually changing global environment, and the related need to negotiate, test, refine and share complex responses to the resulting challenges. This signals the need to re-evaluate organizational structures and processes to ensure that they appropriately enable these new activities at all levels. We propose approaches to KM incorporating concepts from Complexity Theory leading to the adoption of a network-centric paradigm in organizations, complementing or replacing traditional hierarchical bureaucracies. In this paper we depict information as data in any media that is available and may be processed to be interpreted by people and thus potentially inform. Knowledge, on the other hand, can be distinguished as the human capability to interpret information and use it creatively, both individually and cooperatively, to add value to human activities and products. This accords with the Macquarie Dictionary definition of 'social capital' in terms ofthe investment in institutions, quality relationships and interactions that enhance wealth making processes. Issues raised by these changing environments are: * Transformations in what constitutes wealth and what is valued; * The shrinking distances around the world and global competition; * The compression of time, which increases the tempo of our lives; * The alteration in the distribution of power, including the power that is accrued by custodians of information. Traditionally, wealth creation was based on land, capital and labour. Now, information and knowledge are major ingredients (Benkler, 2006). Creating wealth is about adding value by turning these new ingredients into products and exchanging them through open global markets. …

Power law distributions in pattern dynamics of attacker-defender dyads in the team sport of Rugby Union : phenomena in a region of self-organized criticality?

Abstract: In the region of self-organized criticality (SOC) interdependency between multi-agent system components exists and slight changes in near-neighbor interactions can break the balance of equally poised options leading to transitions in system order. In this region, frequency of events of differing magnitudes exhibits a power law distribution. The aim of this paper was to investigate whether a power law distribution characterized attacker-defender interactions in team sports. For this purpose we observed attacker and defender in a dyadic sub-phase of rugby union near the try line. Videogrammetry was used to capture players’ motion over time as player locations were digitized. Power laws were calculated for the rate of change of players’ relative position. Data revealed that three emergent patterns from dyadic system interactions (i.e., try; unsuccessful tackle; effective tackle) displayed a power law distribution. Results suggested that pattern forming dynamics dyads in rugby union exhibited SOC. It was concluded that rugby union dyads evolve in SOC regions suggesting that players’ decisions and actions are governed by local interactions rules.

Framework for the Role of Self-Organization in the Handling of Adaptive Challenges

Abstract: This paper extends the concept of self-organization from the natural sciences to management and proposes a framework for the role of self-organization in the handling of adaptive challenges by enterprises. The process of self-organization is a characteristic of those complex adaptive systems that are far-from equilibrium, and results in the creation of order in a system by the internal interactions between agents leading to stronger adaptive capability. This paper presents a synthesis of the concept of self-organization suitable for management with communication as its central focus. Results from an empirical study in three Australian small and medium sized enterprises (SMEs) indicate that an adequate level of three key factors-trust level, open communication and strength of the value system in an enterprise-is needed for self-organization to occur. Introduction Managers today are faced by an increasingly turbulent and unpredictable environment with rapid changes in the market place (Morgan, 1988; Merry, 1995; Brown & Eisenhardt 1998; Pascale, 1999; White, 1999; Meyer, Gaba & Colwell 2005). The changing reality for organizations has led to the long-held Newtonian paradigm in management being challenged by the view offered by chaos theory (Zimmerman, 1993; Wheatley, 1994; Tetenbaum, 1998; Tasaka, 1999). Chaos theory offers a view of the world as dynamic where change is the norm not the exception, and where prediction is impossible. Complexity theory contends that organization can arise spontaneously and is adaptive (Frederick , 1 998). In the natural world "astonishingly simple rules, or constraints, suffice to ensure that unexpected and profound dynamical order emerges spontaneously", and this self-organization has been termed "order for free" (Kauffman, 1995: 74). Self-organization along with selection creates a system's capacity for adaptation to environmental conditions (Kauffman, 1993:173). A number of disciplines in the natural sciences have led the study into the concept of self-organization - for instance chemistry (Prigogine, 1968, 1976; Nicolis & Prigogine 1977; Prigogine S Shalizi, Shalizi H Jantsch, 1980; Kauffman, 1993, 1995; Camazine et al 2001). While the idea of self-organization has been taken up by management theorists (Weick, 1977; Foerster, 1984; Ulrich & Probst 1984; Drazin & Sandelands 1992; Comfort, 1994; Molleman, 1998, 2000; Biggiero, 2001; Stacey, 2001), there is a limited amount of empirical research - indicating a gap in the understanding of complexity science's application to the social world. Researchers have pointed out the need for appropriate consideration when applying concepts from the natural sciences to the social sciences. As Comfort (1994) notes "the concept of self-organization needs to be redefined and reinterpreted in order to assess both its presence and functions in the performance of social systems in rapidly changing environments." This paper extends the concept of self-organization from the natural sciences to management using a cross-disciplinary approach and examines this theoretical framework in light of empirical results. Need for Applying Complexity Theory to Management Numerous books aimed at management practitioners (Peters, 1989; Goldstein, 1994;Watson, 1994; Wheatley, 1994; Nonaka & Takeuchi 1995; Axelrod & Cohen 1999; Haeckel, 1999; Rails Jr. & Webb, 1999; Wood, 2000) and the general public (Waldrop, 1994; Merry, 1995) have raised awareness for the potential application of complexity theory. However as Mitleton-Kelly (2003) notes, "by comparison there is relatively little work on developing a theory of complex social systems despite the influx of books on complexity and its application to management". The dynamics of complex adaptive systems, such as their ability to evolve and learn over time, can provide organizations with insights into developing the capabilities to handle adaptive challenges. …

Moving Far From Far-From-Equilibrium: Opportunity Tension as the Driver of Emergence

Abstract: Complexity scholars have identified two distinct catalysts of emergence: (1) Far-from-equilibrium dynamics that trigger order creation, and (2) adaptive tension (McKelvey, 2004) which can push a system toward instability, leading to the emergence of new order. Each of these provides a necessary but incomplete explanation of the catalyst for emergent order. In particular, the far-from-equilibrium framework, when taken to its logical ends, would conclude that most dynamic and fluid organizations are the ones farthest-from-thermodynamic equilibrium - like Exxon or GM, for example. Adaptive tension on the other hand identifies an exogenous force of market change, but doesn't explain how emergence is actually triggered. As a solution I propose "Opportunity Tension," which integrates the endogenous intention of an entrepreneur to create a new venture to the exogenous changes that open up an entrepreneurial opportunity - a market that will exchange money for the value being created. Opportunity tension occurs in "pulses," each cycle leading to a new dynamic state of the system. This model, which is consonant with the notion of "dynamic creation" (Chiles et al., 2010), contributes to a complexity science that is moves us beyond a far-from-equilibrium framework. Introduction In our search for the driver of order creation, management scholars have developed two contrasting catalysts of emergent order: far-from-equilibrium dynamics (e.g., Meyer et al, 2005), and adaptive tension (e.g., McKelvey, 2004). Although on the surface these two approaches seem similar, technically the constructs are different in significant ways, which have important implications for an organization science of complexity. The more common approach for describing the origin of new order is through the onset of "far-from-equilibrium" dynamics. Far-from-equilibrium approaches "elucidate the non-linear mechanisms that actually drive [discontinuous] change forward" (Meyer et al, 2005: 470a). In this theoretical framework, organizing far-from-equilibrium is what leads to " . . .emergence and ongoing, perpetual novelty" (Meyer et al, 2005: 450b). Choi, Dooley and Rungtusanatham (2001: 356) also use this framework to explain the origin of systemic state change, arguing that such change is triggered " . . .when the system is far from equilibrium." In a similar way, Browning, Beyer & Shetler (1995) show that the emergence of an alliance was sparked by ". . .a period of disequilibrium [which required the semiconductor industry] to operate qualitatively differently than in the past." In sum, a range of authors focus on how far-from-equilibrium processes catalyze the emergence process. On the other hand, McKelvey has offered a different explanation for the driver of emergence, namely adaptive tension. In McKelvey's understanding of Prigogine's dissipative structures theory (Nicolis 8c Prigogine, 1 989), order creation is caused by and initiated through "energy differentials" which are imposed onto the system. New order is created when one of these energy differentials crosses athreshold (McKelvey, 2004: 319): "...[when] an imposing energy differential, what I term adaptive tension, exceeds... the lower bound of the region of emergent complexity." Plowman and her colleagues build on McKelvey's formulation, suggesting that periods of organizational instability are often ". . .full of adaptive tension and tension grathents; it is in this state that emergent self-organization and creative destruction occur" (Plowman er al, 2007: 520). In these models, adaptive tension is the push, the catalyst, the driver that initiates a dynamic state that leads to emergence and order creation. In sum, we have a bit of a conflict around causality: What actually initiates the emergence of new order? In the far-fromequilibrium approach, the entire system moves into a regime that is away from equilibrium; this "far-from-equilibrium" organizing leads to non-linearities and perturbations or experiments that generate novelty. …

Complexity and the Phenomenological Structure of 'Surprise'

Abstract: Surprising, unexpected events happen all the time which can be thought of and addressed in a variety of ways. On one hand, surprise can be something that is not desired, something suppressed or controlled for. Or, it can be something that is embraced, sought out or encouraged. Conceptually speaking, conceiving surprise in this fashion is not uncommon. Still, there seems to be an important piece missing from the many discourses on surprise. This paper offers some a possible framework to understand the experience of surprise in relation to a more complexified framing of the lived-experience. Specifically, by drawing upon principles from the complexity sciences, this paper considers the lived human experience of surprise as an emergent phenomenon that arises from a complex system. Introduction The phenomenon of surprise is typically understood to be a special event that happens to people where the surprising event may be thought of as a good or a bad experience. Moreover, for many people who manage or direct the actions of others in an organization (like many workplaces), surprises are seen to be unwelcome moments that sometimes evoke a feeling of discomfort, prompting the need to know more, plan better, or design better systems to avoid the possibility of any unexpected surprises (McDaniel et al., 2003; Weick & Sutcliffe, 2001). Certainly, it is possible to attend to many different kinds of organizations to find examples of how human expectation and certainty, as well as, unexpectedness and surprise, affect and shape our knowledge and actions within those organizations (Stacey, 1992). It is not, however, the aim of this paper to explore organizational contexts for an understanding of surprise. Rather, it is the idea that surprise and unexpectedness are particular kinds of experience. In the complexity science literature, however, the experience of surprise is not, by and large, something that is discussed, but is taken up in slightly different ways - often, for instance, psychologized, framed by the notions of bounded rationality and a lack of information (Simon, 1991). Nevertheless, it is the aim of this paper to suggest some connections that can be made between the experience of surprise and a complexity science interpretation of surprise. As such, in this paper, I outline a view of "surprise" and "unexpectedness" framed by the philosophical branch of phenomenology, further interpreted through the lens of complexity and some of its attendant principles. In doing so, this paper seeks to illuminate the notion of surprise by tending to the common ground of complexity and lived-experience. Phenomenological Research: Finding A Methodological "Middle Ground" Phenomenological research might appear to be antithetical to the work of science. After all, whereas matters of methods and technique, which are quite antithetical to the spirit of phenomenological research, are not fitting in any purely prescriptive sense (van Manen, 1990), science requires some attention to methods of study and tools and techniques: scientific techniques can create a certain illusion of control and knowability, but then difficulties and tensions can arise. For example, as Barritt (1983: 4) and her colleagues write, "techniques of collecting information are more important than what the collection is all about-that is, the thing one is trying to understand. There is too much talk about things which matter very little-numbers, methods, contexts stripped of their meaning in the name of research-and too little about the important events-the real stuff of life...". To suggest, however, that science is the same as methodological reductionism, in the derogatory sense of the word "reductionism", is short-sighted. In fact, any sort of methodology - the scientific method, for example - in a prescriptive sense, as when one follows a recipe, is bound to fail to capture in any real sense the messiness of doing research, particularly research about livedexperience. …

Sustainable Supply Networks: A Complex Systems Perspective

Abstract: A complex adaptive systems perspective is used to examine the sustainability of the supply network in the commercial aerospace manufacturing sector. A framework for the analysis of coevolutionary dynamical change is used which examines the structure, integration methods and process dynamics within the supply network. Multiple methods are used for data collection from 8 firms in the sector. The frame work identifies 14 management implica tions related closely to the sector’s current heterarchical supply network archetype. The management implications address known environmental factors for the sector and the broader techno-economic paradigm.

Making Room for Affordances

Abstract: This is a conceptual paper about 'affordances' It is inspired by Gregory Bateson (1972) who argued that consciousness is a person/environment interactive process; we will focus on how relationships between environments and organisms lead to perceived possibilities, actions, and cognition Both the relationships between environments and persons, and the relationships between persons and environments count The connection between world and consciousness is dynamic There is a mutual causal link between circumstances and organisms We argue that the world via affordances presents itself to consciousness Emergent possibilities afford; complexity affords Scott Kelso's 'complementary relating of contrarieties' affords Affordances are the dynamic reciprocal relationships between animate persons and their environments Affordances are in-between-their cognition is situated and contextual Affordances are the a next frontier for organization studies Introduction Profoundly influenced by Micheal Foucault and Jacques Derrida, most social complexity theory has focused on the knowing languaged-based subject (Cilliers, 1998) In Foucault's terms, (social) cognition is grounded in the historically specific episteme Complexity scholars have asserted that the contemporary episteme is complexthat is, it is emergent, dynamic and resembles a strange attractor The episteme characterizes the dominant way of seeing, and operates as a very socially and economically powerful hermeneutic The contemporary episteme, Foucault argued in the mid -twentieth century, has centered on 'discipline' and 'power' - ie, it focused on the ways of structuring physical and mental existence prerequisite to the development of technology and industrialization, physical and social science, bureaucracy and globalization, material wealth and post-Fordist capitalism Derrida focused specifically on how texts emerge from one another, refer and defer to one another, and form complex webs of signification Text-based consciousness (which includes art, music, mathematics, etcetera - ie, a great variety of forms of text) - it is asserted, is the only form of consciousness that we (can) know Text is emergent, dynamic and operates in webs of relationship and preassumptions While a social complexity theory grounded in Foucault and Derrida is revelatory; it is very perception and consciousness directed The danger is that 'world' gets lost in some sort of consciousness studies Without wanting to trash the Foucault-Derrida interpretation of complexity theory, in this article we focus on 'affordances' - ie, not on language, but persons and circumstances It is our conviction, with Bateson, that complexity involves a multi-dimensional ecology of world and consciousness, objects and perception, opportunities and language We examine here the concept of 'affordances' - the assumed mechanism(s) whereby complexity presents itself as (weak) signal(s) to consciousness The "World", in the form of affordances, invites response by subjects Affordances act as attractors drawing humans into action Humans are not in a universe of dead material (hyle), but live in a world of active subject- world inter-relationship (s) The world acts, makes occur and initiates possibilities 'Affordance' is a word for this activity This article does not debate epistemologica! or research methodological issues Obviously experience would remain unheard of and unanalyzed if it could not be named As Karl Weick keeps repeating, what actually is cognitized and named undergoes sensemaking after-the-fact (Weick, 1997) But as Bateson insisted, language and existence (ie, knowing and world) are two dimensions (or two sides of the coin) of the same existence Of course, the world that we can know at any one point is deeply determined by our assumptions, thought processes and mental characteristics; but our mental structure(s) have to do with the materiality ofthe brain, the makeup of our environment and the history of our universe …

Temporality in link prediction

Abstract: This article describes research into the discovery and modelling of emergent temporal phenomena in social networks. It summarizes experimental results that bring together two views in contemporary science: Bayesian analysis and link prediction, to enhance the current understanding of emergent temporal patterns in social network analysis (SNA), particularly in value creation through social connectedness—an important, and growing, discipline within management science. Traditional link prediction methods use the values of metrics in a graph to determine where new links are likely to arise, and little work has been done on analyzing long-term graph trends. We have found that existing graph generation models are unrealistic in their prediction, and can be complemented through the use of temporal metrics, in the study of some networks. To date, no temporal information has been used in link prediction research, thereby excluding valuable temporal trends that emerge in sociogram sequences and also lowering the accuracy of the link prediction. We extracted information from the Pussokram online dating network dataset, and 9,939 cases of each class were formed. Logistic regression in the Weka data mining system was used to perform link prediction. Our results show that temporal metrics are an extremely valuable new contribution to link prediction, and should be used in future applications. In addition to using metrics to measure the local behaviors of participants in social networks, we used Bayesian networks to model the interrelationships between the metrics as local behaviors and links forming between individuals as emergent behaviors (social complexity). We also explored how the temporality in Link Prediction: Understanding Social Complexity E:CO issue Vol. 11 No. 1 2009 pp. 69-83

Social Entrepreneurship Effects on the Emergence of Cooperation in Networks

Abstract: Even in simple contexts, the dynamical interaction between agents creates complex features. The presence of agents of change affects dramatically the underlying social structure. Some agents seem to be important in shaping the evolution of interactions: traditionally, these agents have been referred to as leaders; nevertheless, recently scholarly interest has been attracted by social entrepreneurs. Do social leaders and social entrepreneurs act differently? Can a social entrepreneurship culture, one that aims for a large number of social entrepreneurs, be welcomed? This paper presents a model of interaction among agents in a community, and sheds light on the catalytic role that some individuals have on the social structure. The results provide some implications about the role of social entrepreneurs and the differences between social entrepreneurship and leadership. Introduction The language of social entrepreneurship may be new, but the phenomenon is not. The interest in it has been rising in recent years, and other forms of social entrepreneurship, beyond that occurring within the nonprofit sector, have also grown. The concept of social entrepreneurship is gaining popularity, but, at the same time, the term has undertaken several meanings, and many authors agree on the fact that this can be confusing (Dees, 2001; Austin et al, 2006; Mair 8c Marti, 2006). For this reason, social entrepreneurship is still an emerging area for academic inquiry. Theoretical supports have not been sufficiently explored, and contributions to theory and practice are necessary. In addition, boundaries of social entrepreneurship to other fields of study remain fuzzy. Several contributions have been proposed in order to clarify the topic. Among others, Austin (2006) presents studies of collaborations in social entrepreneurship, such as alliances and networks, hoping for interdisciplinary research. Empirical approaches examine the sociological aspects behind the exploitation of social entrepreneurial opportunities, deriving from the existing entrepreneurship theory of opportunity. Robinson (2006) considers the relationship among three factors: the decision to enter a particular market, the social networks in which entrepreneurs are embedded, and the existing types of institutions which can help the development ofthe initiative. Considering the integration of sustainability and the environment, Clifford et al (2006) suggest that successes related to the mission-driven values and ideals and to creating networks of mutual benefiting stakeholders. Following Austin et al (2006), the social entrepreneur must focus on building a network of contacts, developing the skills to manage the different relationships in this network effectively. Furthermore, networking across organizational boundaries seems to be essential, because the goals of creating social value do not imply that value can be captured within boundaries. An interesting case which emphasizes this aspect is studied in Rhodes and Donnelly-Cox (2008). We do not intend to put much emphasis on social entrepreneurs as individuals, focusing on personality traits that may contribute to entrepreneurial success. Rather, we are interested in what social entrepreneurs do; in fact, it has been already observed that the right question to ask is not "who the entrepreneur is" (Gartner, 1988). Furthermore, as Light (2006: 50) underlines, the available evidence suggests that success depends less upon personality than on teachable skills. According to Light's definition, social entrepreneurships "can also come from small groups or groups of individuals, organizations, networks, or even communities that band together to create pattern -breaking change". Moving away from who becomes an entrepreneur to what they seek, the number of social entrepreneurs expands. The level and the intensity of social entrepreneurship can vary greatly: because of continuous changes in circumstances, this activity might pause, stop and restart. …

Knowledge Transformation, Learning and Changes Giving Competitive Advantage in Aerospace Supply Chains

Abstract: The paper investigates three levels of learning—adaptive, reactive and expansive—for the transformation of knowledge to enhance innovation and competitive advantage in commercial aerospace supply chains. A perspective of supply chains as complex Activity Networks is used for data analysis based on in-depth interviews in a global setting. Themes for the interviews were identified through rigorous literature research. The paper provides evidence of levels of learning in commercial aerospace supply chains. We found that a) adaptive learning brings a supply chain up to present industrial standards only, b) reactive learning makes the supply chain competitive, and c) expansive learning gives the supply chain potential for competitive advantage. By considering supply chains as the interaction of different work activities, the forces of change can be better understood. The findings may be use ful to practitioners in understanding the importance of different levels of learning to supply chain sustainability.

Emergence Is Not Always 'Good'

Abstract: The concepts of emergence and collective intelligence are fascinating, and from their study might come good things. But neither is 'good' by definition and we ought to be careful not to let our enthusiasm and interest lead to us into speaking too casually about the benefits of 'encouraging emergence' or 'developing collective intelligence'. We can find ourselves battling the emergent properties of a system, and working against its collective intelligence. This article explores an example to illustrate this from the field of social care. It also discusses some tentative 'laws' and some issues resulting from the positive nature of popular perspectives on emergence. Introduction The ideas of emergence and collective intelligence seem to be inherently attractive ones. When we read about ants solving a problem, or people being wiser as a group, we think in positive terms. That Wikipedia can even exist, never mind that it can sometimes be the best source of information on a subject, is surprising and wonderful. The thought that collective intelligence might be useful in finding a way forward on global warming is something worthy of detailed study. From here it takes only one small step to a place where we are talking about how to encourage emergence and how to develop collective intelligence. We find ourselves thinking about these things as being inherently positive attributes of a system, particularly of a human one. Two of the four introductory paragraphs in Wikipedia's entry on collective intelligence (20 August 2008), to which I'm referring for obvious reasons, specifically present this positive slant (the other two are neutral). But to take this step is, I think, a huge mistake. The fact is that emergence and collective intelligence aren't 'good' by definition. If good things can emerge, so can bad; and intelligence can be put to beneficial or to detrimental use. This article explores these points in more detail, examining some specific examples, suggesting some general conclusions, and discussing the consequences that arise from it being popular to take a positive view of emergence. I'm aware that some readers might resent any implication that they didn't already know that emergence and collective intelligence can result in 'bad' as well as 'good' - so I should be clear that I'm not necessarily presenting new knowledge here. We've known about the awkward ways in which systems work for a long time. I'm simply reacting to the manner in which emergence and collective intelligence tend to be discussed. A Simple Example I find this much simplified example helpful as an introduction to this discussion. An advice centre is staffed by passionate specialist workers. They individually reply quickly and efficiently to telephone queries. If they can answer the query directly they do so, and if not they immediately say so and refer the matter quickly to their colleagues. They each care deeply about getting the right replies sent to people. We might expect that the emergent properties of this system - which is made up of passionate and efficient workers - would be positive. Unfortunately we all know that this isn't how emergence works. Putting a group of efficient and passionate people together doesn't necessarily create an organization which, in our dealings with it from outside, is efficient and passionate. When we look at our interaction with individual workers, we find we are dealt with efficiently and the worker's passion is clear. But we may also find that we are passed repeatedly around the system, that our query is never actually answered, and that it takes a long time for us to work out that the centre does not have the expertise we need. The property of being inefficient at replying to queries is an emergent one. It is one that arises at, and is best observed at, the organizational level. It's clearly not a 'good' property. A More Informative Example Amore in-depth example is required if we are to look at this properly - and I'll refer to the area of work in which I specialize, which is in supporting change within 'care' organizations. …

A Dynamical Model for Information Retrieval and Emergence of Scale-Free Clusters in a Long Term Memory Network

Abstract: The classical forms of knowledge representation fail when a strong dynamical interconnection between system and environment comes into play. We propose here a model of information retrieval derived from the Kintsch-Ericsson scheme, based upon a long term memory (LTM) associative net whose structure changes in time according to the textual content of the analyzed documents. Both the theoretical analysis carried out by using simple statistical tools and the tests show the appearing of typical power-laws and the net configuration as a scale-free graph. The information retrieval from LTM shows that the entire system can be considered to be an information amplifier which leads to the emergence of new cognitive structures. It has to be underlined that the expanding of the semantic domain regards the user-network as a whole system. It hints an epistemological shifting from the ontological models to the ontogenetic ones in describing knowledge dynamical representation. (ProQuest: ... denotes formula omitted.) Introduction The concept of an intelligent agent involves a world description by which the agent makes its choices, activating some data evaluation strategies and selecting the most significant elements on the basis of a given objective and the interaction with the environment. The old strong AI approaches were based on formal logic tools and heuristic rules for achieving a sufficiently exhaustive world description. Expert Systems used in various fields - chess, military and economic strategies, clinical diagnosis etc. - even though they used different formal tools for conceptualization, they were all classifiable within the strong AI and they shared a static knowledge representation form. A world description made up by a set of well defined and ever accessible production rules inevitably leads to a limited efficacy as the semantic domain is expanding. Connectionistic approaches based on neural networks and parallel distributed processing systems have opened new prospects characterized by a different and closer relation between the system and the environment. The new systemic-cybernetic approach by N. Wiener, L. Von Bertalanffy, R. Ashby, H. Von Foerster,, H. Maturana and F. Varela makes a world description depending on the observer. This is considered to be an interacting system that selects knowledge according to its inner structure and its dynamic history. Between the observer (the intelligent system) and the observed (the world) there is no longer a linear and deductive relationship defined by a formal model, but there is a circular function based on a continuous adaptation and coevolution strategy. This implies at least two important differences from the approaches adopted in strong AI. A relationship of thermodynamic openness between the system and the environment must be considered. Information and energy make up a flow that crosses and continuously modifies their relationships and their structure. This dissipative feature ofthe system is only a necessary but not sufficient condition. The system must also have a logic openness i.e., it must show emergent behaviors which depend on the dynamic state of the relationship between the system and the environment. These emergent behaviors should lead the systern to more complex adaptive situations by the production of new knowledge. So there are no longer definite and independent knowledge representation forms of the agent-observer world description. But the agent continuously generates adaptive processes that produce at any moment a world description bound to the structure and the objectives of the agent. Classic AI representations conform to the adoption of models with a low logic openness. They can be seen as samples of a knowledge structure that is always dynamic in the natural systems. Being aware of these limits and problems we are searching for common aspects between the symbolic and the connectionistic approaches. …

A Review of the Dialectical Tragedy of the Concept of Wholeness: Ludwig Von Bertalanffy's Biography Revisited

Abstract: A Review of The Dialectical Tragedy of the Concept of Wholeness: Ludwig von Bertalanffy's Biography Revisited written by David Pouvreau reviewed by Jeffrey Goldstein published by ISCE Publishing ISBN 978098 1703282 (2009) Revelations Ludwig von Bertalanffy is of course well known as the founder ofthe systems approach known as "General Systems Theory" (GST), and for his many publications in this area as well as in inaugurating and leading several of the large systems sciences associations. GST certainly ranks as one ofthe important precursors to modern complexity theory by setting the conceptual stage for many important developments in the latter. This excellent biography of Bertalanffy written by David Pouvreau (translated from the French by Elisabeth Schober) was prompted by two precipitating events. The first was the advent of several recent scholarly German language biographies of Bertalanffy written as doctoral theses. This research uncovered aspects of Bertalanffy's early career and work during WWII in Vienna. The second was the accidental discovery in 2004, in a second-hand bookstore in Buffalo, New York, of a large portfolio containing many letters sent by and received by Bertalanffy plus numerous books, preprints and related materials. These two events enabled Pouvreau to avoid a hagiography on the one side and a deprecating trashing of Bertalanffy on the other, an intention that appears, to this reviewer at least, to have been superbly accomplished. But before getting into some ofthe details Pouvreau's biography I feel the need to admit that I came to this review with two dispositions regarding Ludwig von Bertalanffy and his work that have affected my reading of the biography. The first is that I have always found general systems theory (GST) to be a bit too, well, general for my tastes. Accordingly, GST has often left me rather cold with its, what I take to be, overly abstract formulations of its basic principles about the dynamics of systems, to such an extent I've often felt a particular dissatisfaction whenever reading anything of Bertalanffy. In that regard, Pouvreau's biography has been very helpful in fleshing-out some of these abstract GST principles by discussing their origin and development during Bertalanffy's long career. One case in point is that, whereas previously I had believed one of the limitations of GST lay in its not only not containing anything like the idea of emergence, I couldn't even see where there was could be any conceptual room in GST for emergent phenomena at all, I was pleasantly surprised to learn from reading Pouvreau's book that Bertalanffy and GST did in fact possess a cognate idea of emergence which he called "supra -individual entities" or "integrations of higher order" about which I'll say more about below. There were other similar, pleasant surprises about his work as well which I will also remark upon below. The second confession is that I knew very little about Bertalanffy's life before reading this biography, only the very broad outlines of it in relation to his general systems theory, which, again, never sparked me to want to read more about him. Consequently, much of this biography came as a complete revelation to me, particularly Bertalanffy's undeniably close association with Nazism, an association that appears to have been of one cloth with his overall manipulative, petulant, opportunistic, and entitled personality dynamics, at least as Pouvreau has depicted the latter through his access to the many letters recently discovered. To be sure, Bertalanffy was no rabid Antisemitic Brownshirter denouncing Jews at every opportunity. But he certainly didn't hesitate to emphasize the pure Aryan heritage of his Hungarian nobility forebears (the "von" of his name), and to manipulate his varied affiliations with Nazism in ambitiously furthering his career in Vienna, and equally manipulative to quickly deny the extent of his flirtation with National Socialism during the denazification period after the War. …

Unpredictability and Nonlinearity in Complexity Theory: A Critical Appraisal

Abstract: This piece explores potential problems with the focus on unpredictability and nonlinearity within complexity theory Whilst not completely rejecting the application of ideas of nonlinearity and unpredictability within the social sciences, I argue that greater empirical and conceptual care is needed The arguments made are illustrated by a critical examination of cases from John Urry's Global Complexity, including the dominance of the petroleum-fuelled car in the 20th century and the prevalence of wild-fires in Malibu Empirically speaking, I argue that claims about particular instances of nonlinearity and unpredictability in the social world must be backed up by appropriate evidence, rather than analysts simply assuming that all social phenomena have these characteristics Conceptually speaking, I suggest that care needs to be taken to distinguish genuinely unpredictable phenomena from those that are simply poorly understood at the present time I also argue that predictability should be seen as a matter of degree Introduction It's arguable that the emphasis in complexity theory on unpredictability and nonlinearity did not surprise social scientists, especially social theorists, but reinforced what they already believed The existence of nonlinear and unpredictable phenomena may have come as a shock to those natural scientists who bought into a (roughly) Newtonian picture of a deterministic universe1 But the vast majority of social theorists, and some empirical social scientists, already believed that the social world was not predictable and could not be understood by undertaking linear operations on quantitative information about social phenomena Defenders of interpretive social thought, (eg, Winch, 1 990 [1958]) social constructionists (eg, Gergen, 1985), and even quasi-naturalists such as critical realists (eg, Bhaskar, 1979), have argued against the possibility of scientific prediction in the social domain, and have preferred instead to focus on qualitative change and the richness of unquantifiable meaning This is not to say that complexity theory adds nothing to previous understandings of the social world, and at the very least complexity theory brings its own vocabulary and explanatory repertoire to account for the social world Furthermore, complexity theory offers concepts that are held to apply to both the social and natural worlds, rather than arguing for a clear-cut division between the two in the manner of interpretive social thought2 Nevertheless, given the extent of common sympathies between complexity theory and its precursors, it maybe that certain shared claims have not been as rigorously interrogated as they might otherwise have been If many are already convinced that the social world is an unpredictable place, and one in which social changes and developments rarely, if ever, have a 'linear' character, then claims by complexity theorists in these areas may have been given a relatively easy ride In this article I intend to build on the small amount of work of those who have questioned, to a greater or lesser extent, complexity theory's treatment of prediction and linearity in the social world (see McLennan, 2003, 2006; Thompson, 2004) My aim is not to attempt a demonstration that the social world is, contrary to complexity theory, fully predictable and linear in character Rather, my intention is to argue two things: firstly, that notions of unpredictability and nonlinearity need to be used in a more careful and analytically precise way then they are in certain existing complexity arguments; and, secondly, that we need to carefully attend to the ways in which empirical cases actually do, or do not, fit in with ideas of unpredictability and nonlinearity as formulated in complexity theory Given the limitations of space, I will focus only on the work of one prominent exponent of complexity theory in the social sciences, John Urry, and particularly on his Global Complexity (2003) …

Understood Complexity: Ibsen's 'An Enemy of the People'—On Complexity, Sense-Making, Understanding, and Exit/Voice/Loyalty

Abstract: 'Understood complexity' is a term of Albert Hirschman (1976) whose economicpolitical theory of 'exit' ('vote with your feet') versus 'voice' (feedback or use your influence for change) (1970), has often been used to (try to) understand whistleblowing (Alford, 2001; Maclagen, 1998). Real complexity is not linear and cannot be adequately studied an model of 'A causes B'. Complexity entails 'A causes B' in a situation wherein 'B causes A'. Bateson in his 'ecology of the mind' understood the circularity of the hermeneutic of complexity; while Weick did not in his theory of sense-making. I argue in this article, via an examination of a play of Ibsen, that circular thinking spiraling towards new insight (s) is much more a possibility of literature (studies) than of social science. Social complexity theory needs (at least partially) I believe to methodologically merge with literary studies. Introduction: An Enemy of the People: A Drama on Whistleblowing Literature is an indirect phenomenon. On the one hand, it is a product of the author's artistic imagination; on the other, it represents aspects of our lives and the world in which we live. I will explore Ibsen's representation of complexity in his realistic drama An enemy ofthe people (1882). The plot takes its point of departure in the discovery that the water at a Spa is polluted and in a concerned employee's unsuccessful attempt to make the management take action to stop the pollution. The plot develops as a case of whistleblowing. Ibsen exposes the organizational response to the whistleblower, resulting in the whistleblower's persecution and in retaliation against him. To my knowledge, the drama is the first literary work to make the pollution of the environment, the struggle for environmental protection, and whistleblowing, its central issues. My focus is on how Ibsen dealt with the complex (organizational) phenomenon of whistleblowing and making moral sense of it. Whistleblowing is discussed in major research literature, starting about 100 years after Ibsen wrote his play (Bok, 1981; Elliston et al, 1985; Petersen 8c Farrall, 1986; Alford, 2001; Johnson, 2002; Bowers et al, 2007; Micely, Near 8c Dworkin, 2008). There is no consensus either on the term 'whistleblowing' or on the role of morality involved. I understand 'whistleblowing' as an act of an employee to make information on illegitimate practices within an organization known to the responsible management and if necessary going to the public with that information. The whistleblower grapples with 'exit' (to leave the organization and/or avoid conflict) and 'voice' (make the problematic situation known), wherein 'loyalty' (to the organization, to the society at large and/or to one's own morality) plays a major role in what gets decided and/or happens (Hirschman, 1970). Ibsen's drama mirrors in many ways the chaos and complexity of whistleblowing. As a work of dramatic art, the play has a clear plot and a tight structure composed in five acts, which suggests order, balance and meaning. I believe that the harmony ofthe form helps the reader make sense ofthe complexity and chaos ofthe content. The play, I will argue, offers 'understood complexity'. The drama takes place in a small town at the Norwegian coast. The prosperity of the town is based on the running of a spa - the Baths, which every summer attracts a lot of visitors and patients. The doctor at the Baths suspects that the drinking water is polluted. He sends samples to a university laboratory and his suspicions are verified. He then sends a report to his superior, the chairman of the board of the Baths, who also is the Mayor of the town. In this report he makes it clear that the water is poisonous and may cause sickness amongst the visitors. The water pipes need to be renewed. He also breaks the bad news to his family and friends, among them Hovstad the editor of the local newspaper, and Aslaksen the representative ofthe local small businessmen. …

The emergence of the communicative value of silence

Abstract: Within traditional theories of communication the silence is often devoid of any communicative value. When the latter is taken into consideration, it is viewed as depending on the intentionality of the agent producing the communicative act. Unfortunately there are diverging opinions about the role to be attributed to intentionality. Moreover, its detection by the receiver is often difficult or impossible, a circumstance which prevents from building a theory of a number of interesting communication phenomena. We hold that the previous problems can be dealt with by resorting to a systemic view in which communication is nothing but a macroscopic phenomenon, emergent from the interactions between elements of a communicative system. This perspective allows to introduce the methodological tools of Systemics to better describe all kinds of communication, grasping their emergent meanings. Only in this way the emergent communicative value of silence can be detected. Such an approach is endowed with a strong potential usefulness when dealing with the communicative interactions within both small and large organizations. Introduction Human communication is characterized by its multidimensionality, stemming from its cognitive, social, cultural, economic, political implications, as well as from its close interconnection with human actions. This implies a plurality of different approaches to communication, each one emphasizing specific features, as a function of particular goals and disciplinary competencies. For this reason we still lack an universally accepted definition of communication and this fact gives rise to a confusion about this subject, which makes difficult any attempt to transform theoretical statements into practical applications. Historically, all attempts to build a general theory of communication have been dominated by two main influences: the one of Shannon and Weaver's traditional Information Theory (1949) and the other based on the psychological perspective introduced by Watzlawick and his coworkers (Watzlawick et a/., 1967). However, they were pushing towards opposite directions. As regards Shannon and Weaver's approach, it undoubtedly offered a firm theoretical basis to the study of communication, being deeply grounded on classical statistical thermodynamics. Its shortcomings derive from the fact that the latter theory entails a strictly unidirectional view of communication process, described as a signal flow from an emitter to a receiver through a medium. Within this context most aspects of human communication are lost. For instance, silence, being coincident with absence of signals, does not communicate. Watzlawick's psychological perspective tries to remedy the flaws of previous approach by introducing the concepts of intentionality and of behavioral interaction, so as to include within the category of communication processes all behaviors occurring within a given interactional context. However, while this approach allows to account for a number of important communication processes, impossible to describe within Shannon and Weaver's theory (this time silence is a form of communication), it fails to give a precise definition of communication. And such a circumstance, in turn, prevents from building a useful theory of communication, as even the subject of this theory cannot be defined. We thus arrived to a sort of impasse: while, relying on principles of classical physics, almost nothing is communication, on the contrary, relying on psychology, all is communication. In such a situation, all theorizing about communication risks to be useless, being poorly grounded even the starting point of all arguments. In this paper we hold that a way for sorting out from such an unpleasant state of affairs is to adopt a truly systemic view. Within it, communication is identified with a macroscopically emergent process, coming out from the interactions occurring between the elements of a communicative system. …

The dark side of organizational knowing

Abstract: Complexity and Philosophy Rigorous investigation of organizational epistemology, or what can an organization know and why, is a sadly underdeveloped field. Knowledge management as a field has suffered from naive assumptions about what knowledge is and how it can(not) be shared. David Seidl in E:CO (2007) made a significant contribution to organizational epistemology, which I want to further problematize. Seidl made two assumptions: one ontological namely that organizations know things; and one epistemological namely that knowledge can be defined as perceptual complexity reduction. I wish to counter that persons and not organizations know things and that knowledge is more social than perceptual. I will argue that the problem of social knowing is not so much grounded in the epistemological question of knowledge / nonknowledge - that is, in the relations of foreground and background, facts and assumptions or knowledge and hermeneutics, as in the much more radical circularities of eternal return (duration) and the continual (re-)founding of social order. I will be inspired for the first point by Pierre Klossowski and for the second by Michel Serres. Introduction Social complexity theory assumes that the interactions, boundaries and limits to knowledge, power and organization are interrelated, nonlinear and emergent. Knowing is situated, circumstantial and contextdriven. Acknowledgement of complexity leads a reduction in confidence in knowledge. By denying or repressing complexity, one can seemingly boost 'confidence' in knowing. The status of knowledge can be maintained by making the nitty-gritty of the processes of knowing invisible. If ontological, epistemological and hermeneutic assumptions are hidden, knowing can appear to be automatic, self-evident and faultless. David Seidl in E:CO (2007) argued for just such a process of complexity reduction in order to facilitate organizational knowing and knowledge management. I wish here to argue against it. For David Seidl the dark side to knowledge is what in deconstruction goes by the name oi aporia (Derrida, 1998). Perception or knowing is only possible thanks to an unseen means of observation. Either a person sees with her or his eyes or the person looks at her or his eyes (for instance, in a mirror) - but one cannot see (for instance, the landscape) and see the instrument of seeing (i.e., the eyes) at the same time. The means of perception have to be unseen in order for the seeing to occur. Likewise knowledge requires assumptions. To know requires that key assumptions remain hidden. A well-know practical illustration of this theme is the observation that one cannot all at once conceptualize riding a bicycle (for instance, the rules of physics involved), and actually ride a bicycle. Performing an activity requires hidden assumptions. Seidl calls this epistemological dilemma the 'dark side of knowing'. Knowledge exists thanks to its nonknowledge. For an empiricist, who does not want to acknowledge the hermeneutic nature of knowing and/or perception's inherent dependence on means of perception, the focus on the aporia is epistemologically challenging. And Seidl's illustrative paradoxes are deeply engaging - knowledge demands the ability to address nonknowledge, growth in knowledge always produces more nonknowledge, higher levels of awareness or metaknowledge simply produce higher levels or more abstract types of conceptual blind spots. But by putting his analysis on almost purely epistemological level, Seidl does not address the relationships between power and knowledge or the social nature of knowing. And I think that the dark side of knowing is at least as much a social issue as an epistemological one. Merging the conceptual worlds of knowledge (epistemology) and power (organization) often in the name of Foucault (McKinlay 8c Starkey, 1998, Knights, 2002) has become commonplace. Emperie [empire] and emperique [empirical] (both Old French) are entangled in what I wish to call emperi(qu)e or following the etymological line to English: empire + empirical = empir (e)ical. …

A Fractal Approach to Sustainable Networks

Abstract: This paper qualitatively illustrates how and why interdependence becomes significant in building coherent and sustainable network systems based upon human flourishing. Ethnographic case data of an icon tourism destination is provided to examine the structure, process and patterns that are essential for understanding network organization. The notion of fractals has been applied to more deeply understand the multi- dimensionality of networks. Through the fractal characteristic self- similarity, the data revealed aspects of volume-filling, reciprocity and enfoldment that were central to the transforming power of network organization. Behind the divisible there is always something indivisible. Behind the disputable there is always something indisputable. Chuang-Tzu Introduction In the world of measurement and deduction, objects are separated into small detail in order to be understood through a quantifiable process of analysis, known as Cartesian and Newtonian scientific approaches. While the outcome of these mainstream approaches has been an industrial revolution of scientific innovation, the disconnect that results from this separation remains consequential (Bohm, 1980). For example, our earth is organized by a string of social, ecological and environmental connections between people, place, space and other life forces. Production of our own food, clothing, warmth and shelter connect us intimately with the cycles of nature. However, our modern industrial system has disconnected us from our environment and we have lost this intimate awareness. Conventionally we see nature as something to be controlled, as a malevolent intrusion on our world, with potentially disastrous consequences that may emerge from this absence of intimate awareness (Snyder, 1990). Thus, we need additional lenses to understand this interconnected world of relationships, processes, patterns and context that Capra (2005) claims are central to the coherence and integration of network systems. Yet our knowledge of such complementary approaches is emergent and raw. Drawing on the new science of complexity, this paper examines fractals as one possible perspective that may assist us in understanding how the intimacy of interdependent processes are not only connected but are also self-organizing and mutually constructed. Fractals demonstrate patterns of interconnection, and while they are primarily mathematical models, this paper applies the fractal concept to an organizational context to qualitatively illustrate how and why interdependence becomes significant in building coherent network systems. This paper identifies themes from a review of fractal literature and illustrates how they apply to an organizational network system. The contribution from this paper is its translation of hard science into an 'observable reality'. Network Interdependence Networks are claimed to be a defining contributor in reshaping our social communities in the 21st century (Parkhe et al, 2006). Yet their complex multidimensionality has signalled the inadequacy of our current linear and predictive theories (Galaslriewicz, 2007). In most network analysis, there is a preoccupation with structure (the nodes and connections) through the relations amongst actors and their individual positional location. These 'measurement' orientations designate a group of relationships that are fixed and controllable which are contrary to the distinctive characteristics of networks. Rather, their plural pathways whereby information is transmitted through any possible connecting point (as opposed to more vertical and horizontal forms in hierarchies) (Powell et al, 2005), their nodal complexity whereby networks become interdependent based upon complementary resource transfers obligating one organization to another (Uzzi, 1997; Gulati, 1999) and the fluidity that emerges from this structural complexity when partners 'switch on the dance floor' (Powell et al, 2006), each indicate complex, flexible and non-linear modes of selforganization. …

An Alternative Sports Metaphor for Understanding Teamwork as Complex: Soccer

Abstract: As critiques of and dislike for organizational teamwork increase, alternatives must be sought for both pedagogy and practice Competitive sports metaphors are often used in management practice and teaching; unfortunately, these tend to reflect distinctly American values of zero-sum competition, cybernetic, error-correcting efficiency, individualistic success, therefore de-emphasizing what (American) organizational teamwork needs most: creativity, innovation, genuine autonomy and inventiveness This is precisely what makes both the pedagogy and practice ineffective This essay proposes the game of soccer as an alternative metaphor and heuristic device I contend that both organizational teamwork and soccer are quantum phenomena Specifically, I demonstrate how soccer teamwork is nonlinear, holonic, emergent and engaged, and articulate those concepts with extant, conventional understandings of teamwork in organizations My hope for the essay is that the soccer metaphor will inspire a more complex understanding of organizational teamwork as a collaborative (rather than simply cooperative or coordinated) activity Want Better Teamwork? Watch More Soccer! We need to ground the reality of "we'3 in a new conceptual structure Danah Zohar, The Quantum Self In the past few years, both the principle and practice of "teamwork" has come under increased scrutiny as one of those organizational practices that in wide variety of ways, has not lived up to its expectation for organizational effectiveness While there has been some success for project teams - designed for a specific purpose and then no longer necessary - other kinds of teamwork, such as that needed by management and executive decision-making teams, seems much more difficult to achieve It's hard to find workplaces that exemplify teamwork Susan Heathfield (2008) contends, for example, that: We have miles to go before valuing teams and teamwork will be the norm" In America, our institutions such as schools, our family structures, and our pastimes emphasize winning, being the best, and coming out on top Workers are rarely raised in environments that emphasize true teamwork and collaboration As we typically understand teamwork, it is not a natural process Our prototypes for teamwork are derived primarily from collectives of talented individuals who manage to coordinate their individual talents for large sums of money "Team" is the name given to these groups of individually talented people Second, we may not be disposed to teamwork Chris Argyris's famous thesis about Model I and Model II governing assumptions (eg, Argyris, 1994) suggests that we are almost anthropologically predisposed to compete for zero-sum outcomes (Model I) rather than collaborate for positive sum outcomes (Model II) We do, according to Argyris, have an idea of what teamwork is, in the sense of genuine collaboration, but we seldom activate that idea Third, as conventionally practiced in organizational and business contexts, teamwork is a managed, "performed" process; it has goals, roles, expectations, objectives, reporting procedures and cybernetic mechanisms to insure efficient progress along a stipulated path toward goal accomplishment This makes it simply coordinated or cooperative activity, rather than collaborative activity In a nutshell, these three reasons would account for its failure Genuine teamwork requires a level of participation and collaboration notusually requisite to specific project accomplishment Importantly, it is this "next level" of knowledge, skill and ability that distinguishes genuine collaborative teamwork from simply cooperative or coordinated activity Unfortunately, we only have a vague idea of what this next level is or entails The concepts most often used to capture or frame - teach and understand - this next level, seem almost magical, celestial, or otherwise unreal, unattainable, or out of our control: synergy, jazz, improvisation, oiflow …

A Review of "Complexity and Policy Analysis; Tools and Methods for Designing Robust Policies in a Complex World"

Abstract: A Review of Complexity and Policy Analysis; Tools and Methods for Designing Robust Policies in a Complex World edited by Linda Dennard, Kurt A. Richardson and Goktug Morcol reviewed by Lasse Gerrits published by ISCE Publishing, USA ISBN 9780981 703220 (2008) Introduction For some years, complexity theory gains popularity in the realm ofthe social sciences, organization studies and management studies. However, complexity theory in the domain of public administration and policy analysis is still a minority interest. There are some authors who find that complexity theory has an added value to understanding public policy processes and public decision making, among others Peter Allen, Tony Bovaird, Henk Wagenaar, Phil Haynes, Walter Kickert, Goktug Morcol, Mary Lee Rhodes, Geert Teisman and your reviewer. Some books have been published and some journals issued special issues, such as the special issues of Emergence, Complexity F Organization (vol. 7, issue. 1, 2005), Public Management Review (vol. 10, issue 3, 2008) and Public Administration Quarterly (vol. 32, issue 3, 2008). However, applications of complexity theory in public administration have mostly received a rather lackluster response by mainstream scholars. Complexity theory for public policy analysis is, in the words of Professor Christopher Pollitt, weak and does not add anything to what is already known: "So, we must ask, what is it that complexity theory adds? Certainly not the ideas of dynamism, or of unforeseen events, because both these are fully present in more traditional accounts. Another tendency found among complexity theorists [...] is to claim as new some concept or insight which has in fact been arrived at previously by researchers working in one or more quite different theoretical traditions. [...] these elements do not yet amount to a theory of complexity in public administration, in the sense of a propositional explanation of actions and outcomes." (Pollitt, 2009: 229). In other words: there is a clear need for more consistent, high quality work on complexity in public administration. Such research needs to transcend the level of metaphors and to move beyond merely copying some of the core concepts that originate in physics and chemistry because, regardless of their beauty, there are fundamental differences between physical and social reality. Also, it should bring additional explanatory value that other theories would not be able to deliver. It is against this background that ISCE published this book. It is to my best knowledge so far one the few elaborate books on complexity to emerge from the domain of public administration and its arrival is timely given the rising interest in complexity theory from this domain. But will it stand the criticism voiced by, among others, Pollitt? Goal and Structure of the Book This book is an edited volume. The first versions of the chapters in this book were presented as papers at the First International Seminar on Complexity and Policy Analysis in Cork (Ireland). The scope of the book is relatively broad and the different topics are arranged under different headers. There is ample attention for the nature of complexity theory (or complexity theories, as pointed out correctly by Morcol) and the implications of complexity for public policy. There is attention for (agent-based) modeling, and also for qualitative case studies. The editors state that the group of authors of this book resembled a "microcosm of global complexity" (2008: 2). They are correct. The book covers a wide range of topics and offers many different approaches to complexity. The editors also state that the book is first and foremost a reflection of a learning process that the group of authors went through. First Impression For someone trained in public administration and with a strong focus on complexity, the book offers a familiar yet challenging read. Many of the authors manage to connect the ideas of complexity theory with more common notions and theories in public administration such as policy network theories and rational choice theories, and that is a welcome addition to the existing literature for two reasons. …

The Application of the Dimensionality Perspective in Organization Study

Abstract: Previous research suggests that organizations may apply two opposite complexity mechanisms to cope with environmental uncertainty: absorption and reduction. However, except for some anecdotal evidence, there is no theoretical skeleton established to integrate these two opposite mechanisms in one framework and to prescribe the contingent conditions for employing them. This paper deconstructs organizational complexity at the organizational elemental level and establishes framework that incorporates three dimensions-organizational complexity, organizational dynamism, and organizational variability. This paper also discusses the environmental conditions for applying absorption and reduction mechanisms as well as the benefits and costs of applying these mechanisms. This dimensionality perspective provides a new avenue for researchers and practitioners to understand and handle organizational structuration issues. Introduction Organizations are often viewed as open and complex adaptive systems that are dependent on their interactions with environmental resources (Wholey ck. Brittain, 1989). Previously, two complexity mechanisms have been recommended for organizations to produce a better adaptive capability: reduction and absorption (Cohen, 1999). The reduction mechanism suggests that a firm should standardize its internal processes and simplify organizational systems so as to decrease the number of external agents that it has to face (Anderson et al, 1999). The absorption mechanism, in contrast, suggests that when facing external uncertainty, firms should complicate their systems in order to create a variety of compound options and risk-hedging strategies (Boisot 8c Child, 1999). To date, no theoretical framework has been built to reconcile these two apparently conflicting logics. We therefore propose a dimensionality perspective to address this issue. The dimensionality perspective focuses on the structuration (i.e., complexity, dynamism, variability) of an organizational system. Applying this approach, the two mechanisms will be deconstructed and analyzed at the organizational elemental level and incorporated in one framework. We plan, by centering our discussion on the organizational complexity dimension, to illustrate how to apply the new dimensionality perspective in organization studies. We aim to make two contributions to complexity research. First, this is the first project that looks at organizational complexity phenomena from a dimensionality perspective, and this new perspective helps to reconcile some contradictory findings in previous studies. Second, a thorough benefit-and-cost analysis of organizational complexity will be presented. The antecedents (i.e., environmental complexity and environmental dynamism), the interactions of three organizational dimensions (i.e., complexity, dynamism, variability), and the outcome of these interactions will be systematically discussed to create a theoretical framework to integrate two opposite mechanisms - absorption and reduction. Organizational Complexity Ashby (1956) proposed the Law of Variety to set the foundation for studying organizational complexity. In his book Introduction to Cybernetics, Ashby stated that in order to cope with environmental variety, firms needed to complicate their organizational structure because only when organizations were more complicated, could they understand, predict, and even "destroy" less-complicated external variety. Following this logic, researchers began to investigate how well a complex organization could cope with environmental uncertainty. Burton and Forsyth (1986) used 14 factors to measure the complexity of organizational structure and processes, including the number of products, the number of product categories, and the number of countries in which operations were conducted, and identified a positive relationship between organizational complexity and firm performance. Damanpour (1996) also found that in general, organizational complexity contributed to organizational innovative capability. …

Adjacent Opportunities: Social Networks Emerge-You Have Nothing to Lose but Your Loneliness!

Abstract: Social networks aren't becoming a part of our culture - they are our culture. And as never before, as societies and organizations are more connected, they are finding that they also have to be more responsive to the emergence of this new collective empowerment. It is the underlying resonance within social networks that keeps them vital, connected and which provides their members access to emergent opportunities that in turn deepen the participant's own connectedness. Once that penetration space has been achieved, the interaction possible within the social network no longer needs to be influenced from outside, and the experience becomes addictive and selfgenerating. However, if the shared resonance were to end, the system would also decay or need to regroup under a new connective banner. Identifying this resonance is not just about listening to language or conversation, although it is certainly expressed and manifest in those terms. The connective tissue of social networks is something more basic than patterns on a page. It has do with identity and not the kind that is easily stolen, but in this case willingly shared. Social networks are the antithesis of the image of the rugged individualist. They are more an appeal to recognize our interdependence, our collective need for each other to survive. The complexity of the information web we are weaving has increased to a level that it is actually pushing us closer rather than isolating us further. And within that drawing together the normally background and inaudible resonance has found its voice. The question I am asking, is will the product of our interaction within social networks - our conversations - experience a similar emergent propensity, out of which innovation can arise? Or will these networked conversations produce only a continued state of ordinary novelty? I find it quite amazing that with the advent of Twitter, an extraordinary marketing tool gone haywire, we also find a proliferation of face-to-face networking opportunities, often documented in real time via Twitter. Go to a conference or gathering arranged by MiIlennials or anyone under the age of 35 for that matter, and three-fourths of the time is allotted to networking and one-quarter to more traditional forms of presentation. And 100 percent of the time tweating to those not there about what they are missing. It will be interesting to see if this continual state of social network connection can deliver the kind of emergent opportunities that are truly innovative. Oded Noy, the founder of a company that has produced a rather remarkable software product, called Social Approach that can identify what he calls "bonfire" conversations taking place within social networks, and then recognize the patterns in those conversations so that companies can use that information and market directly to that conversation, thinks that this new public discussion is filled with ample emergent opportunities. The uniting force of these "bonfire" conversations for Noy, is the underlying resonance of the topic at hand. He believes that "the synchronicity of the individuals interacting within the network conversation provides a much broader group dynamic and opportunity for emergence, which ultimately will have a much greater impact on the world in which we are living. …

Networks, Emergence, Iteration and Evolution

Abstract: Recognition that the reductionist approach to science leaves great gaps in our understanding has led to the synthesis approach to further explain the world around us. The synthetic approach examines the inter-relationships of individual entities as they interact to create complex networks. This approach spawned the creation of a new science-the study of Complex Systems. This article takes the concepts of Complexity Theory and hypothesizes a process of simple steps iterated many times over that explains the emergence of new entities and the evolution of our Universe. The concept of systems, emergence, iteration and evolution is proposed to explain the process underlying our evolving Universe. This process would be expected to leave fractal patterns in its wake. The fractal patterns are related to the shared tendencies for self-organization found in complex networks. The principles apply to all networks irrespective of their component parts and include both inanimate and living systems. Introduction What good is a worldview if it does not explain the events around us in a meaningful and internally consistent manner that helps us to understand and resolve the problems that we continually face? This work proposes a worldview that conceives of everything as a process of interacting systems acting on hierarchical, evolving levels. This process is theorized to be fundamental to the evolution of the Universe. Thus, the Universe is seen as being built out of layers of complex dynamic systems, each layer consisting of the 'emergent properties' that are generated from the complex interactions of the systems in the level below it. The Universe is everything we know and think - and probably more. It definitely includes particles, forces, and energy. The subject matter of physics, chemistry and biology as well as the abstract notions of forces, ideas and information are all aspects of the Universe. A truly universal 'law' describing the Universe must apply to them all. This article discusses a process that seems to be universal and accounts for the development and evolution of all that was - and will be - 'new' in our Universe. Hopefully, most of the ideas expressed in this article are not new. They have already been developed, discussed and published in the scientific literature by others. The uniqueness of this article lies in the emphasis on the relationship of emergence and the iterative process with the fractal patterns observed throughout Nature, and proposes that this relationship underlies the evolution of the Universe. Systems, Networks, Emergent Phenomena, Iteration and Evolution A. Single entities (E) interact to form complex interacting systems. New behavior traits emerge (emergent phenomena) from the dynamic interplay of the components of the system... B. The newly emerged properties define the system. The original system may no longer be viewed as a system, but rather as a single new unique entity (El). C. A detailed description of the characteristics of this new entity (El) is a description of the emergent properties that define it. A1. Single entities (El) interact to form complex interacting systems. New behavior traits emerge (emergent phenomena) from the dynamic interplay of the components of the system. B1. The newly emerged properties define the system. The original system may no longer be viewed as a system, but rather as a single new unique entity (E2). C1. A detailed description of the characteristics of this new entity (E2) is a description of the emergent properties that define it. A2. Single entities (E2) interact to form complex, etc., etc., etc. The process of smaller systems interacting to create new 'emergent' properties that define new entities - and then these entities forming complex networks out of which newer 'emergent phenomena' arise is hypothesized to be the process by which the Universe evolved. It is an iterative process. …

Collective case study method and fractal geometry: Instrumental and intrinsic cases in organizational research

Abstract: In case study methods of organizations, researchers are often limited to the aggregation of individual cases within the context of the organizational case Borrowing from Stake's (1995) use of instrumental and intrinsic case studies, this paper presents a fractal geometry case study method For the purposes of this article, on site interviews of seventeen librarians who work in a research institution were conducted to learn more about their experiences with organizational change Instrumental case studies of these individuals, or rather those cases that respond to other phenomena, were performed and analyzed at the micro level A clustering technique, serving as a fractal seed, was also incorporated to draw out themes that highlighted the interconnections of individuals These cases were then recursively integrated into an emergent framework of the intrinsic case of the organization The use of this method suggests that observations of individuals, and the subsequent meaning they generate at the micro level, reflect the complex interconnections of these cases At the same time, this method suggests that the recursive integration of individual cases contributes to the understanding of the complex organization at the macro level Introduction Case study research methods of organizations have been debated with some frequency in the social and behavioral sciences In addition to the case findings, epistemologica! differences are represented in the disparate approaches performed by case study researchers Moreover, there are ontological differences between case studies thought to be traditionally scientific and those case study methods that are purely qualitative As an example, some scholars approach case study research as a means to generalize the findings of a case or a collection of cases to the population at large Arguing that this must be avoided in qualitative research, however, Stake (2005) focuses primarily on the specificity of cases and how their uniqueness contributes to further understanding "Case study has been too little honored as the intrinsic study of a valued particular, as it is in biography, institutional selfstudy, program evaluation, therapeutic practice, and many lines of work" (Stake, 2005: 448) In case study methods of organizations, researchers are often limited to the aggregation of individual cases within the context of the organizational case Conversely, narrative research methods are often chosen to focus on the individual but may sometimes lack coherence in their connection to the case of the organization Borrowing from Stake's (1995) use of instrumental and intrinsic case studies, this article presents a complexity science research methodology, using a fractal lens for case study method For the purposes of this article, on site interviews of seventeen individuals who work in an academic unit of a research institution were conducted to learn more about their experiences with organizational change Instrumental case studies of these individuals, or rather those cases that respond to other phenomena, were performed and analyzed at the micro level A clustering technique, serving as a fractal seed, was also incorporated to draw out themes that highlighted the interconnections of individuals These cases were then recursively integrated into an emergent framework of the intrinsic case of the organization It is the purpose of this paper, therefore, to show how this fractal case study method suggests that observations of individuals, and the subsequent meaning they generate at the miero level, reflect the complex interconnections of these cases At the same time, this method highlights the recursive integration of individual cases and how they contribute to the understanding of the complex organization at the macro level as a fractal form Theoretical Orientation The theoretical orientation of this paper draws from the scientific and social sciences research of complexity theory while grounding qualitative case study method in the works of Stake (1995, 2005, 2006) …

A Review of New Approaches for Modelling Emergence of Collective Phenomena: The Meta-Structures Project

Abstract: A Review of New Approaches for Modelling Emergence of Collective Phenomena: The Meta-Structures Project edited by Gianfranco Minati reviewed by Steven E. Wallis published by Polimetrica, Milan, ITA ISBN9788876991431 (2008) This book is part of a larger research agenda. At its core, that agenda is to develop a better understanding of "emergence" and "self organization" in a general sense, in order to more effectively model those processes and generate new insights in a variety of disciplines. The purpose of this book is to support that agenda by describing a new approach to modeling processes of emergence and selforganization. One major problem to understanding emergence (or anything, for that matter) is that we humans tend towards reductive thinking. That is, we want simply, straightforward questions and answers. It is very difficult, in contrast, to understand phenomena at a deeper level. That is why the scientific revolution took centuries instead of days. Even to say, "Look, that is emergence" is fraught with issues of recursion, reduction, and emergence - all tangled up in some unknown way that can never be completely captured - or reduced. This habit of reductive thinking leads to stunted conclusions that are dead-ends to scientific inquiry. For example, if we were trying to understand the behavior of birds, we might ask, "How many birds are required to make a flock?" Most people would immediately argue about numbers: A flock must have more than one... Is two enough to make a flock?... Certainly 30 is plenty... Is a thousand birds something different - a 'megaflock?'. . . Should we differentiate between micro-flocks and mega-flocks?... What about birds without feathers? ... and so on. These kinds of discussions have bedeviled the social sciences for centuries, producing much heat and little light. The same issue experienced by social scientists has been repeated in the process of computer modeling. Our human tendency to focus on "things" leads us to create and use programs to replicate our point of view. We create "agents," and they acquire "resources," move across "landscapes," exhibit "behaviors," and occasionally learn things. We change the names from "people" to "agents" but they amount to the same thing. We are not so much finding something new, as we are trying to replicate something that already exists we are just calling it by a different name. Minati's approach, in contrast, draws on insights from physics as well as the social sciences to describe an approach for changing how we understand - beyond the simple renaming of obviously observable phenomena. In part one, Minati lays out a set of important definitions including relations, interactions, structure and systems. Then, delves into the difference in modeling between homogenous and heterogeneous models. This includes a discussion of the similarity of agents compared with the rules under which they operate. The discussion on systems differentiates between organized, evolutionary, non-structured, and self-organized systems. Then, delves into approaches to modeling noting the limitations of each version of systems. He makes the case that existing approaches are limited in ways that cannot be overcome by applying (for example) more computational power. Just as a system must have openness to its environment to survive and thrive, there must be an openness of the investigative process. Emergence, it is said, is too complex to be understood through reductive approaches. Minati argues, "Reductionism is considered as the adaptation of an unsuitable level of description which confuses necessary and sufficient conditions for the establishment of such a system" (p. 35). For example (not from the book, because the book does not provide enough examples) if we see the earth beneath our feet, and it does not move, we might conclude that our Earth is the unmoving center of the universe. However, such a view ignores a wide range of alternative understandings; and, further, that reductive belief inhibits the development of more knowledge. …

Editorial: Chaos and Complexity, Branches and Trees, and the Advantages of Vague Concepts

Abstract: E:CO has a New Subtitle This issue signifies the start of volume number 1 1 , a feat showing not just how far E:CO has come in a relatively short amount of time, but gives striking evidence that it is still only building steam. Just look at the rich contents of this issue: articles hailing from diverse countries around the globe and covering a wide variety of fields, and all done in a rigorous, thought-provoking fashion. This issue also inaugurates a new subtitled for E.CO: "An International Transdisciplinary Journal of Complex Social Systems." That's quite a mouthful but it more accurately reflects what E:CO is about and is evolving more and more into. Three phrases in our new subtitle stand out. First, there is the emphasis on "International." Of course, internationality was essential in the journal's mission right from the start, and even before that with the precursor journal Emergence. But we're now including it in the title to reaffirm our commitment to a truly international focus in terms of our authors, our readers, and our article topics. Indeed, this very issue is like a mini United Nations with authors hailing from Australia, Italy, Egypt and Israel (our own little peace initiative although it wasn't planned that way!), South Africa, China, and the US. Then there's the term "Transdisciplinary". Again that was an indispensible aspect of our initial mission but here it is spelled out overtly. This current issue is a vivid demonstration of transdisciplinarity as can be seen by considering the fields of research of the authors: cognitive science, urban planning, environmental studies, architecture, knowledge management, social networks, organizational theory, operations research, information technology and computer science, and sociology. Moreover, the methods and constructs utilized herein similarly cutacross a wide range of scientific, mathematical, cultural, and philosophical disciplines. The third new addition to the subtitle is the intentional mention of "Social Systems." We are in fact the only existent journal specializing in complexity applications to social systems. But of course we also supplement this specialization with numerous phalanges into philosophy, mathematics, education, physics, biology, semiotics, and many other fields related in one way or another to social systems. In spite of the long and still unwinding road that E:CO and complexity studies in general have been on, it comes as a surprise to me how vexing it can be to pin down, into a concise definition, just what is it that the field of complexity theory or complexity science covers. I'm sure many of the readers have experienced a version of the following situation. I was recently at a party where I met some friends that I hadn't seen in 30 or more years. To catch up on all those years, we asked the customary questions of what we each had been up to personally and professionally. When my turn came, I again, as I always do in this situation, became tonguedtied as to how to describe just what it is that complexity theory is about. This elusiveness of a crisp definition of complexity always reminds me ofthat famous retort by one of the United States Supreme Court Judges when asked how he defined obscenity: he said he couldn't define it precisely in a few words, but he knew it when he saw it! That's how I see the study of complex systems, I can't precisely define it but I'm pretty sure I can recognize it when I see it. Of course, this kind of response is not particularly enlightening and it brings with it all the vagueness accompanying ostensive, contextual, and indexical references, a topic I'll return to below. Moreover, although one tactic could have been to start listing the varied types of systems considered to be complex, I realized that this would not be of much help. Since, not only were there too many of them - with the result I would be hard pressed to say what they had in common besides such unilluminating vague properties as interconnectedness - it would be questionable whether many of the systems I mentioned were in fact complex, or at least complex all the time, i. …