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.
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.
It is possible to understanding the spatial behavior and structure of cities based on urban morphology alone. The units of analysis are urban clusters, defined as contiguous built-up urban areas instead of municipalities defined by politically determined boundaries. By means of historic data of the Tel-Aviv metropolis we present analyses of urban cluster statistics from 1935 to 2000. We focus on the largest cluster which includes the city of Tel-Aviv and several surrounding municipalities. The results suggest anomalies in the years 1964 and 1985. Based on the character of cities as self organizing systems, our study suggests that the analysis of urban cluster dynamics is an efficient tool to study urban phenomena.
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.
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.
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).
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.
Cause, condition, and effect characterize the process of emergence, but often times, rather than focusing on this, enterprises would rather spend billions trying to predict and divine what is going to happen in the future. How effective has this fortune telling been? The answer is costly and not very accurate. However, that fact hasn’t seemed […]