Complexity science as order-creation science Œ Emergence: Complexity and Organization

Traditional ‘normal’ science has long been defined by classical physics and most obviously carried over into social science by neoclassical economics. Especially because of the increasingly rapid change dynamics at the dawn of the 21st century, different kinds of foundational assumptions are needed for an effective scientific epistemology. Complexity science – really ‘ordercreation science’ – is particularly relevant because it is founded on theories explicitly aimed at explaining order creation rather than accounting for classical physicists’ traditional concerns about explaining equilibrium. This article sets up the rapid change problem, and shows why evolutionary theory is not the best approach for explaining entrepreneurship and organizational change dynamics. New theories from order-creation science are briefly presented. The continuing centrality of models in scientific realist definitions of modern science is brought to center stage. Agent-based computational models are shown to be better than math models in playing the role of forcing theoretical elegance and continuing the essential experimental tradition of effective science. Introduction Isabelle Stengers (2004) reminds us that the founding idea of complexity science was Prigogine’s juxtapositioning of the 1st and 2nd Laws of Thermodynamics so as to explain the emergence of dissipative structures. Implicit in this was his questioning of the reversibility of time and the centrality of equilibrium in “normal” science (Prigogine & Stengers, 1984). There can be no greater foundational challenge to normal science, the origin of which was classical physics. Sandra Mitchell (2004) reminds us of the centrality of idealized, abstract models, one of the enduring legacies of logical positivism (McKelvey, 2002). Ironically, if mathematics is taken as the model-technology of choice, these two foundational statements can’t be joined. Why? Math as the core modeling method of modern science originated in Newton’s studies of orbital mechanics and was greatly reinforced by the Vienna Circle’s founding of logical positivism in 1907 (Suppe, 1977). Given that the 1st Law is about conservation of energy, and that classical physical dynamics is about the translation of matter from one form to another, math became the only means of rigorously accounting for the accuracy of the translation, and thereby the proving of theories about what causes what, given equilibrium. Simply put, math models can’t handle order creation. The methodological invention that does allow the joining of the two foregoing foundational statements is the agent-based[1] computational model. Casti (1997) states that in fifty years time computational experiments will be seen as the primary contribution of the Santa Fe Institute. The tendency in organization studies so far is to focus on explicating the term complexity, seemingly in every way possible, and to rush toward offering practical wisdom, again, seemingly in every way possible. Could it be that the science of it all is being ignored in the rush toward practical application? My intent is to emphasize the science part in two ways. First, calling ‘it’ complexity science is like calling thermodynamics ‘hot’ science. Complexity and hot are the outcomes at one end of the dynamic scale. More aptly, complexity science is order-creation science. This puts focus on the fundamental change in the nature of the dynamics involved – from equilibrium dynamics to order-creation dynamics. Second, there is the changing role ofmodels. Math is good for equilibrium modeling. Agent-based computational models are essential for modeling order creation. My objective is to explain why order-creation science offers significant new lessons for how scholars doing management and organizational studies can better understand the modern dynamics of their phenomena. I begin with a review of why ‘New Age’ economies and organizations in the digital information era call for organizational designs in which the collective intelligences of many employees may be brought to bear, quickly, on New Age organization problems and strategies. I then discuss why ordercreation science offers better ways of understanding and researching emergent collective phenomena. Foremost among these new methods is the use of agent-based computational models. I also outline the epistemological reasons why models remain a cornerstone of effective science. A short overview of how one might use agent models in conducting one’s research on organizations and managerial processes follows. Organizational dynamics in the 21st century 1 Emergence: Complexity and Organization ‘New age’ economics and increased external complexity Over past centuries, economic life has been marked by three revolutions: agricultural, industrial, and service. The 21st century brings with it a fourth – the digital information age. N owhere are the dynamics characterizing the knowledge era more vividly and succinctly portrayed than in a recent book edited by Halal and Taylor (1999), 21st Century Economics: Perspectives of Socioeconomics for a Changing World. Their conclusions (paraphrased) are far reaching (pp. 398-402): 1. Economies of the 21st century will be dominated by globalization and integrated by sophisticated information networks; 2. Increasingly deregulated economies will mirror the textbook ideal of perfect competition (and marginal profits); 3. Creative destruction from the transition will create social disorder worldwide; Nearly autonomous entrepreneurial cellular networks and fundamentally different ways of corporate governance will replace topdown hierarchical control. Part Two of their book, titled “Emerging Models of the Firm,” focuses on the magnitude of the problem managers face as they cope with increasing competitiveness world-wide while at the same time trying to shift from top-down control to the management of complex new organizational forms using radically new approaches of managerial leadership for purposes of knowledge creation and the creation of in-tra-organizational market dynamics. As if this weren’t difficult enough, Larry Prusak (1996: 6) points to speed as the driving element[2]: “The only thing thatgives an organization a competitive edge – the only thing that is sustainable – is what it knows, how it uses what it knows, and how fast it can know something new! ” Halal and Taylor say life will be different on the other side of the millennium – the New Age: “Communism has collapsed, new corporate structures are emerging constantly, government is being ‘reinvented’, entirely new industries are being born, and the world is unifying into a global market governed by the imperatives of knowledge” (1999: xvii) The four conclusions by Halal and Taylor, mentioned above, predict economic revolution over the next two decades. Given this, what should organization theorists and managers worry about? Significant clues come from Part II of their book, boiled down in their Table 1, which focuses on “Emerging Models of the Firm. ” Abstracting from this Table, what do the various authors in Part II see going on? New technology, dynamic markets, accelerating change, shorter product life cycles, digital information revolution, decentralization, globalization, environmental decay; Corporate restructuring focusing on responding to hypergrowth, building cellular networks, product-cycle management, use of internal markets; Disequilibria rather than optimization, with efficiency and enhanced competitiveness stemming from employee empowerment. The authors in Part II emphasize decentralization, cellular networks, internal markets, and employee empowerment as defining elements of New Age economies – all in response to disequilibria and new economic trends. Key questions we face are: How should we research organizational and/or managerial dynamics? How should managers manage? (Drucker, 1999) Also from Halal and Taylor (1999) we learn that New Age trends call for dramatically new organizational strategies and designs. Strategy scholars have seen this coming. Recent writers about competitive strategy and sustained rent generation parallel Prusak’s emphasis on how fast a firm can develop new knowledge. Competitive advantage is seen to stem from keeping pace with high-velocity environments (Eisenhardt, 1989), seeing industry trends (Hamel & Prahalad, 1994) and value migration (Slywotzky, 1996), and staying ahead of the efficiency curve (Porter, 1996). Because of increases in the need for dynamic 2 Emergence: Complexity and Organization capabilities, faster learning, and knowledge creation there is an increased level of causal ambiguity (Lippman & Rumelt, 1982; Mosakowski, 1997). Learning and innovation are not only more essential (Ambrose, 1995), but also more difficult (Auerswald, et al., 1996; ogilvie, 1998). Dynamic ill-structured environments and learning opportunities become the basis of competitive advantage if firms can be early in their industry to unravel the evolving conditions (Stacey, 1995). Drawing on Weick (1985), Udwadia (1990), and Anthony, et al. (1993), ogilvie (1998: 12) argues that strategic advantage lies in developing new useful knowledge from the continuous stream of “unstructured, diverse, random, and contradictory data” swirling around firms. Matching internal to external complexity via bottom-up emergence The foregoing trends appear on a CEO’s horizon as uncertainties. Uncertainty in organizational environments is a function of (1) degrees of freedom (generally taken as the most basic definition of complexity – GellMann, 1994); (2) the possible nonlinearity of each variable comprising each degree of freedom, and (3) the possibility that each may change. These three environmental ingredients give rise to seemingly countless strategic options. Long ago, and seemingly in simpler times, Ashby characterized environments and possible adaptive options using the term “variety. ” His classic Law ofRequisite Variety (1956: 207