elaborates on theoretical advances in organizational processes with specific reference to self-organization, organizational learning, motivation, decision making, and implications for practice and intervention / [discuss dynamical systems school] and summarize the theoretical progress pertaining to organizational theory and OD [organizational development] theories of stability and change in organizations [beyond Lewin's force field, self-organization, organizational learning] / decision making [complex systems require complex controllers, beer distribution and workforce staffing] / motivation and conflict resolution / total quality management (PsycINFO Database Record (c) 2012 APA, all rights reserved)
This volume offers a new and very different approach to exploring leadership, one based on the new sciences of complexity. What we are calling “Complex Systems Leadership Theory” posits that leadership can be enacted through any interaction in an organization. Far from being the sole province of managers and executives, we contend leadership is an emergent phenomenon within complex systems. As such, exploring the meaning and implications of “emergent” is one of the major issues taken up by the chapters in this book. Through advances in computational modeling and non-linear dynamics, the interactions which generate leadership can be “tracked” in a much more rigorous way, enabling managers to better understand and encourage those dynamics of interaction which prove to have beneficial effects on the organization. Overall, we see a Complex Systems Leadership Theory as the core of a new era in leadership studies; introducing and furthering this new era are the primary goals of the present volume. This
Policies to reduce urban poverty are increasingly important, not only in developing but also in developed countries. Yet, urban poverty seems invariant in relation to economic growth. Although different methodol- ogies and conceptual frameworks have surfaced to deal with poverty re- duction, the way to effectively achieve this objective is not clear. In this paper we develop a comprehensive approach to deal with urban poverty reduction policies by making up for the lack of attention to social net- works in nearly all poverty reduction programs or policies. We critically assess this neglect of social network connectivity in two case studies, Favela-Bairro, or slum revitalization, in the city of Rio de Janeiro and a program in workforce development in New York City. We then discuss several of the most important elements of a social network perspective. The aim here is to show why it is necessary for urban poverty production policies to incorporate social network connectivity with the marginal- ized and disenfranchised poor. We offer guidelines as to how this kind of social network connection of the poor with the non-poor populations of our urban environments may proceed.
The concept of causality is revised in the light of the phenomenon of emergence as seen in chaos and complexity theories. Emergence is examined by taking a close look at the arising of new, more complex attractors in the logistic equation as the parameter k is increased. The "qualitative dynamics" of these attractors are understood in terms of a pattern- based causality. Philosophical issues associated with this revised view of causality are then discussed.
This article examines recent attempts to gain insight into philosophical paradoxes through using NDS models employing iterated difference equations and resulting phase portraits and escape time diagrams. The temporal nature of such models is contrasted with an alternative approach based on the a-temporal and non-dynamical construct of a lattice. Finally, there is a discussion of how such strategies for understanding paradox transcend the realm of empirical research and enter territory in the philosophy of mathematics.
A key theme throughout this book, one that sharply distinguishes it from other works in the genre of leadership/management/ organizational theory, is that complexity is not something to be avoided or somehow damped down but instead is capable of yielding great dividends if it is embraced in the appropriate manner. In this chapter, we offer many insights from burgeoning research into social networks, one of the most intense and promising areas of complexity science, in order to show how leaders can reap benefits through transmuting their organizations’ complex social networks into smart networks that play a inimitable role in constructing ecologies of innovation. Smart networks contain this potential since it is through them that the identification and dissemination of experiments in novelty can become the requisite seeds of innovation. At the same time, smart social networks enable rapid adaptation to a relentlessly changing environment.
Emergence refers to the arising of unpredictable, nondeductible, and irreducible coherent structures, patterns, and properties in complex systems. Emergent phenomena are understood as collectivities or integrations occurring on a macro-level emerging out of less integrated substrates on a micro-level. The construct of emergence is turned to when the dynamics of a system can be better understood by focusing on across-system organization rather than by decomposition into parts. As the sciences of complex systems have rapidly expanded over the past three decades, the study of emergence has come forward as one of the most vital areas of research and theorizing, a dramatic shift from hovering on the edge of credibility as it was in the past to being embraced currently across a wide range of sciences and related fields of study.
Innovation—it’s a buzzword for the twenty-first century. Creating new services, new products, new processes, new business models, new organizational forms, and new industries seems to be the key to success in this era of business. What drives innovation? Why do some companies achieve innovation more consistently than others? Is it the people? Is it the compensation? Is it the industry?
The elite sales managers at IBM in the early 1990s were proud to work at the world’s leading information technology (IT) company. But more recently, something had begun to change. Slowly at first, then far more quickly, it was becoming apparent that the company’s prospects had become increasingly bleak. A new technology, the microprocessor, entered the market a decade before, and IBM itself had helped define this new market when it launched the phenomenally successful IBM PC in 1981. All along, IBM’s experts had continued to counsel that the PC would never replace the vaulted IBM mainframe computer. They were wrong. During this period, low levels of interaction resonance (the important idea we described in the last chapter) among the product developers as well as the sales and services teams were setting the company up for a crisis.
The authors present a new approach to leadership based on findings from complexity science. Integrating real case studies with rigorous research results, they explore the biggest challenges being faced in fast-paced organizations, and provide a host of concrete tools for leading during critical periods. © Jeffrey Goldstein, James K. Hazy, and Benyamin B. Lichtenstein, 2010. All rights reserved.
This paper lays-out an approach for probing the nature of complex systems through focusing on parameters in relation to variables and understanding parameters in terms of contexts and constraints. Rather than starting from a set of preconceived abstract principles in order to build up a philosophical conceptualization of complex systems, the paper instead starts with the praxis of working with complex system through the means of modeling, intervening, and leading them. From this grounding in praxis, the paper offers a set of conceptual tools for more effectively understanding and hence working with complex systems, including guidelines into a philosophy of complex systems. This paper is meant to the first of two related papers. The first, the one presented here, looks primarily at the role of parameters in mathematics and the relation of parameters to contexts and constraints. The paper turns to the study of semantics in linguistics as to help unpack the role of parameters and contexts in complex systems. The second paper will present case studies utilizing the conceptual tools developed in the first paper. In particular the follow-up paper will look at various aid programs around the world being used to fight poverty and low quality of life conditions. It is hoped that a complexity science lens can make such programs more effective.
This paper concludes a three part series by reimagining processes of emergence along the lines of a formal "blueprint" for the "logic" of these processes, a topic surprisingly neglected even within the camp of those advocating some form of emergence. This formalism is presented according to the following conceptual strategy. First, the explanatory gap of emergence, the presence of which is one of the main defining characteristics of emergent phenomena, is interpreted in terms of uncomputability, an idea introduced in complexity science in order to supplement the more traditional features of unpredictability, nondeducibility, and irreducibility. Uncomputability is traced back to a method devised by Georg Cantor in a very different context. I label Cantor's formalism a type of "self-transcending construction" (STC), a phrase coined by an early commentator on Cantor's work. Next, I examine how Cantor's STC was appropriated, respectively, in the work of Gödel and Turing on undecidability and uncomputability. Next, I comment on how self-transcending constructions derive a large measure of their potency via a kind of "firtation" with paradox in a manner similar to what Gödel and Turing had done. Finally, I offer some suggestions on how the formalism of an STC can shed light on the nature of macro-level emergent wholes or integrations. This formalism is termed a "self-transcending construction" a term derived from the anti-diagonalization method devised by George Cantor in 1891 and then utilized in the limitative theorems of Godel and Turing.