Jeffrey Goldstein

A Complexity Science Model of Social Innovation in Social Enterprise

Abstract A complexity science-based model for social innovation in social enterprises is presented. The three components of the model include: (1) representing the evolution of social innovation using nonlinear dynamical systems with accompanying parameters and attractors; (2) a cusp catastrophe model of bifurcation or the emergence of a new attractor; (3) the role of emergence in complex systems utilizing recombinatory operations. The model represents the emergence of social innovation as an evolving dynamical system governed by the interaction of two parameters. The first parameter is opportunity tension or the degree of coordination and organization on a collective level required to resolve social problems or take advantage of social opportunities. The second is informational differences having to do with the accessibility of information via social networks connecting key players in the social system under consideration. The informational differences parameter also refers to experiments in social novelty acting as seeds of the emergent social innovations. Since social innovation is understood as the emergence of a new attractor reflecting the social innovations, the new attractor is shown to replace an originary attractor representing inadequate ‘business as usual’ practices and social networks that have not been able to resolve the social problem or take advantage of the opportunity. At a critical threshold, the social system undergoes bifurcation as extant social components are recombined leading to the generation of novel social forms that can more sufficiently resolve the social problem or take advantage of the opportunity.

The Singular Nature of Emergent Levels: Suggestions for a Theory of Emergence

Suggestions are offered for a theory of emergence based on a clarification and new interpretation of the singular nature of emergent levels. These suggestions cover formalisms, formulations, and measurements. In contrast to mere collectivities, as well as the rendering of macro- and micro-levels in entropy formulations, order parameters, and distinctions in temporal dynamics, emergent levels are described as “privileged” and “confounded.” A discussion of the insufficiency of previous formalisms in dealing with the structural novelty of emergent levels sets the stage for the introduction of a new formal construct, that of self-transcending constructions. This construct is linked to the idea of logical depth as a complexity measure. The advantages of a semantic rather than information–theoretic perspective are discussed. In addition, the tendency to confuse levels in models with levels in emergent phenomena themselves is described. Finally, conclusions about emergent levels as a new natural kind construct are offered.

Embracing the Random in the Self-Organizing Psyche

The history of the idea of randomness in the Nineteenth Century is presented as a backdrop to Freuds understanding of randomness in the psyche. Next, the role of the random event in nonlinear dynamical systems theory is discussed. Then, a revision of the traditional psychoanalytic view of the synthetic function of the ego is proposed based on the process of self-organization in which random elements are utilized in the emergence of new systemic structures. A theory of dreaming is elaborated which transcends the traditional dichotomy of dreams as either random events or meaningful psychological contents. Finally, remarks about psychoanalytic treatment as a process of self-organizing incorporation of the random are offered.

Emergence, Self-Transcendence, and Education

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.

Applying Generative Leadership to Your Organization

Throughout this book we’ve described and exemplified how generative leadership informed by complexity science can work successfully in organizations large and small. Each example has drawn out one or more key insights into how complexity science can be leveraged to create ecologies of innovation. For example, we saw how: Netflix grew through “ecological” partnerships that often constrained them at the same time IBM successfully navigated a period of criticalization The SEED program in Indonesia emerged in unexpected ways that were more effective than could have been planned Starbucks’s move into Chicago was facilitated by a four-phase process of emergence Apple, Inc., and Parkside Hospital found ways to “systematize” experiments in novelty Jerry and Monique Stern in identified “positive deviants” in Vietnamese villages, and reframed their marginal behavior into shared knowledge that virtually alleviated malnutrition there and in dozens of other countries June Holley constructed smart networks that dramatically decreased hospital infections, and the U.S. Army now pursues warfare through information networks The list goes on.

Leadership in the Cusp of Change

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.

Creating Ecologies of Innovation

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 Innovative Power of Positive Deviance

In this chapter we begin to describe the specifics involved in creating an ecology of innovation in your organization or community. Thus far we have focused on the workings of complex systems, and we have shown how advances in complexity research over the last quarter century can inform one’s thinking about innovation and adaptation in organizations. In particular, we have pointed to the importance of a kind of leadership that enables change and adaptation in organizations, what we call generative leadership. Earlier chapters described how such conditions can and do encourage individuals throughout the organization to experiment with novel approaches, either in an effort to capitalize on opportunities or to solve problems. We also described how these simple ideas can, under the right conditions, extend and expand a wave of change that spreads across the entire organization. At the same time, we have insisted that these things don’t happen by themselves. Generative leadership is needed to create the conditions that enable success. In this chapter and in the next, we describe specific ways in which generative leadership enables in novation-led success even under difficult and challenging conditions.

Introduction: A New Science of Leadership

During the initial panic of the “Great Recession of 2009” John Chambers, the CEO of Cisco Systems, told the New York Times of a crucial lesson he had learned nearly a decade before from Jack Welch when he was CEO of GE.1 Chambers had asked, “Jack, what does it take to have a great company?” Welch responded, “It takes major setbacks … by that I mean, a near-death experience!”

Experiments in Novelty

At the core of emergence—indeed at the core of our book—is how an ecology of innovation can produce unique experiments that have the potential to become seeds for unprecedented organizational action. This is particularly salient in the high-tech industry over the past few decades where an understanding of the workings of an ecology of innovation can explain a most baffling conundrum: why have some companies thrived while others that possess even more resources failed? Consider, for example, the difference between the rebirth of Apple Computer through the phenomenal success of the iPod and iPhone, compared with the unraveling of the “old” AT&T1 which had been a technological powerhouse at the dawn of the Internet age. Complexity science offers an incisive understanding of why an ecology of innovation took root and was enormously fruitful at Apple but at the same time ran into fatal obstacles at the old AT&T.