Complexity Theory and the Organization: Beyond the
Metaphor Complexity theory offers an appealing metaphor for analyzing organizational behavior. Fundamental to complex adaptive systems (CAS) is the emergence of high-level order from low-level interactions among heterogeneous, autonomous agents, each guided by a few simple rules. To look at business organizations as complex adaptive systems, then, is to see their properties as emerging from the interactions among people in the workplace. The metaphor accords with experience because it suggests that the essence of business organization is what individuals do, not what executives plan. We examine two investigations, both based on this CAS view, but very different in approach. The first uses an agent-based computer model of social behavior known as Sugarscape, developed by Robert Axtell and Joshua Epstein of the Brookings Institution. Axtell adapted this simulation approach to show how the interaction of individuals, following simple rules defining their preferences for work and leisure, relates to the growth, lifespan, and death of firms. The second is a framework developed by Roger Lewin and Birute Regine that categorizes the interpersonal relationships among the members of an organization. Building on assertions of the power of informal organizations, such as Ralph Stacey’s idea of the "shadow organization," they conducted a qualitative study of ten business organizations in four countries, and observed that these informal relationships are the interactions that determine the emergent performance and cultural features of companies. The Problem of Organizational Intervention Much of the work of management—its organizational design, restructuring, and human resources efforts—is aimed at getting people to shift priorities or improve performance. In doing this work, managers have learned that change doesn’t happen simply because they plan or mandate it. Managers are now aware of communication networks, trust, and an array of lessons from psychology confirming that the ways people change are more complicated than behavioralists’ stimulus-response models supposed. And, managers have begun to apply this better understanding of people as they replace traditional illustrations of relationships, like organization charts, with greater acknowledgment of informal organization. The problem with all of these efforts that aim to motivate change is that their impact is difficult to evaluate. In Beyond the Hype, Robert Eccles and Nitin Nohria decry the proliferation of massive organizational change programs with themes like "empowerment" and "culture." The fact is, these programs have not produced proven results, although management consultants eagerly promote them. In Eccles and Nohria’s assessment, these programs contribute to a stream of activities that make management feel it is doing something right; but as the activities build upon one another, they rarely subjected to careful evaluation that might reveal the important relationship between intervention and outcome. We propose that CAS-based simulations will get us beyond the metaphor—that they can become powerful tools for managers if they incorporate the kinds of interpersonal exchanges that drive organizational performance. Moreover, in such simulations, the relationships between parameter characteristics and system-wide behavior can be tested. Simulation, then, shows promise as a proving ground for what have so far been management’s least well understood efforts. The Firm-Building Model Traditional economics has produced models of social systems that are often simplistic and unrealistic. Most are built on assumptions such as populations of rational individuals endowed with perfect information. There is no heterogeneity of agents in such models; they look instead at averages. And each individual in these models makes decisions based on an unconstrained ability to maximize fixed appetites, such as preferences for work and leisure. (The degree to which each achieves its preference determines its "welfare.") Conventional modeling of the formation of firms—their birth, growth, and death—has focused principally on explanations that involve macroeconomic phenomena, such as economies of scale and the terms of trade. By contrast, the complexity theory perspective identifies the micro-domain as the primary explanatory level, with firm building an emergent effect at the macro level. Axtell’s agent-based model tests this latter hypothesis, by allowing agents to decide whether to join a firm based on how well a firm satisfies its work-leisure preferences. In the model, companies grow because cooperation maximizes productivity. But because the fruits of productivity are shared equally within firms, as companies increase in size they begin to attract some agents who are less productive ("free-riders"). When overall productivity falls below levels that satisfy the more ambitious agents, these leave the firm to join more productive ones. The system never reaches equilibrium: firms are created and disintegrate continually. Axtell’s model is clearly based on some of the same simplistic assumptions that underlie traditional economic formulas. As he observed of the work-leisure calculation, "This is a very stylized picture of how humans behave." But it is different in that the agent population is heterogeneous, and agents interact with each other to produce the phenomenon of the firm. Axtell discovered that the firm size distribution the model produced resembled the real world; plotted on a log/log scale, the distribution of company size, he found, was a power law with a negative exponent close to 2. As in the real world, the model’s economy was made up of many small companies and only a few large ones; none of them lived forever. It is remarkable that Axtell’s simple agents create even a narrow kind of verisimilitude. These dynamics appear as changing patterns on a computer screen, and encourage speculation about the kind of culture that might be represented by them. For instance, as individuals leave one organization to join another what overall effect does that have on the firm? and on the larger economy of firms? The real potential of this modeling approach, however, is in identifying which parameters are important in establishing the emergent culture of the workplace and creating simulations that help firms to discover the reliable interventions—in compensation systems, hiring approaches, insourcing and outsourcing, organization structure, etc.—that management should make. The welfare utility function is obviously limited. The work of Lewin and Regine has been aimed at identifying candidate parameters. For instance, their study shows that people join firms for other reasons. And people often stay with a company, even if they could easily join another where they would be better paid, because of loyalty. Loyalty is created by many factors, such as personal relationships, commitment, and alignment with the purpose of the organization. The challenge, therefore, is to discover what behaviors should be used to define parameters outside the traditional economic utility function for modeling organizations. Five Domains of Connection Over the last year, Lewin and Regine interviewed a broad selection of people—from CEOs to secretaries—in ten organizations. Their investigation covered companies diverse in size and business activity, from DuPont, the global chemical company, to a small family-owned chain of paint stores, and from VeriFone, Inc., the high-tech transaction security firm to Broken Hill Proprietary, Ltd. an industrial mining company. The companies they studied were based in the United States, the United Kingdom, Germany, and Australia. Most companies were selected for study because their leaders claimed to be managing according to complexity theory–based principles. Previous interpretations of CAS in management assert that these approaches include an emphasis on self-organization rather than management control; attention to potentially exponential impact of small interventions; and encouragement of churn, redundancy, and conflict, which are seen to move the organization away from a high degree of order and towards the healthy edge of chaos. , , , A few were chosen for study because their leaders (or outside business observers) said they practiced these kinds of management approaches, even though they did not profess to be employing CAS theory. Lewin and Regine listened first to how people described day-to-day life in these workplaces, paying special attention to what interview subjects said they valued most about their organizations. Consistently, they noted, people at all levels of these companies valued relationships highly. Lewin and Regine then hypothesized that relationships were central experiences for their subjects because their organizations were operating explicitly as complex adaptive systems. To further develop their hypothesis, Lewin and Regine next focused the interviews to gather detailed descriptions of these highly-valued relationships. Based on these interviews, they developed a framework that illustrates a nested hierarchy of interactions within and between organizations. They identified five domains of relationship: to oneself, to others, to the organization (or CEO), to other organizations and the community, and ultimately to the global ecosystem (see Figure 1). Lewin and Regine used this framework in assessing the relative health of the organizations they studied. In their view, the more positive the relationships at each level, the more likely companies were to progress toward healthy relationships at the next level. They label the resulting culture one of "care and connection." And although few of the organizations studied achieved healthy relationships at all five levels (most frequently absent was relationship to the global ecosystem), those in which at least three or four domains of relationship were healthy were highly effective workplaces. They found that, in these organizations, companies had more than just a "feel good" social environment; they exhibited high adaptability and good financial results. For managers, Lewin and Regine’s observations offer a new way to consider the impact of attention to healthy relationships. Relationship-nurturing behaviors such as listening attentively, investing time in conversation with employees, and placing high priority on creating opportunities for employees, they suggest, end up having far-reaching impact on creativity, productivity, and adaptability through their effects on the organization’s emergent behavior. A next step for Lewin and Regine was to delineate more clearly the parameters of their framework. They did so by codifying the kinds of statements they heard from subjects describing different levels of relationship. Listening for this language, they believe, will help managers diagnose the status of relationships in their organizations in terms of presence, mutuality, purpose, interdependence, and responsibility (see Figure 1 for elaboration). Lewin and Regine have gone from field observation to categorization, to a start at selecting candidate parameters for modeling. This kind of work will produce many candidate parameters that, brought into to an agent-based model like Axtell’s, could be tested against well-defined measures of performance. This approach has already shown some success in simulations of military units based on a six-parameter representation of each soldier’s priorities. Eventually, we believe, such parameters will replace the crude work-leisure utility function. Lewin and Regine’s framework provides a useful starting point for developing in management simulations what Robert Axelrod calls, "a third research methodology," which combines the inductive skills of students of business organization with the deductive models of hard science. New Modeling Strategies: A Preliminary Exploration We believe that modeling offers a way for managers to test their interventions—something that rarely happens in business life. This kind of modeling involves identifying agent characteristics, relationship dimensions, and figures of merit for organizations. We have suggested that qualitative, field-based work can produce candidate parameters for these. Without yet beginning a simulation experiment, we have tried to imagine how insights from qualitative field work might be married with agent-based modeling work. A manager might want to understand whether one of Lewin and Regine’s domains is dramatically more important than the other four. Each parameter could be scaled from 0 to 1, and individual agents in a heterogeneous population could be endowed with a fixed amount of each parameter, say 0.5 for "responsibility" and so on. A representation of the overall performance of the organization would have to be defined so as to analyze the output of the model—speed of adaptation, rate of innovation, or efficiencies of resource use are examples. The relationship of "responsibility" (and other parameters) to performance could then be tested. Of course a manager playing with this simulation might also want to know what it would take to improve organizational performance. The model could be run to reveal how the culture changes as "responsibility" and other parameters change. By analogy with the Schelling tipping model, we would expect the change in overall performance to be nonlinear, shifting via a phase transition, say when a certain threshold of collective "responsibility" is passed. Alternatively, the parameters for individual agents could be non-fixed, perhaps having a different potential range in each of them. In this model, we could study how a company’s performance might evolve as individuals interact and influence one another at the local level. Agents representing CEOs could be endowed with extra power to influence others. Such a model could also be set up to investigate interorganizational dynamics. For instance, in an economy that rewards those who form alliances with other companies—as in the computer industry—one might compare Company A, which scores high on "interdependence" (external connectivity) but low on "mutuality" (internal connectivity), with Company B, which scores the reverse. Challenges and Benefits Creating truly useful models of human dynamics may still be a long way off. The greatest challenge lies in translating insights into parameters and finally into simple behavioral rules. It is not clear how to translate Lewin and Regine’s framework, with parameters like "presence" measured by rules like "be authentic" (in real life it is hard to agree on what this means; it is judged based on many subtle behaviors), into a type of welfare function. Nor are we certain how to create measures of organizational performance that are both broad enough and discriminating enough to be useful. Another problem may emerge from the success of these models. Because simulations provide powerful visual explanations they can make it easy to forget that models are still vastly simpler than the real human context in the workplace. Models, as Axtell and Epstein urge, should serve as laboratories. Our findings there may teach us a lot about management, but they will not produce behavioral formulas. It is still up to those of us who come together in organizations, or lead them, to put what we learn into practice and estimate its impact in the real world. We see two valuable possibilities in bringing modeling into organization consulting. The first is its usefulness in testing interventions, as discussed above. The second is its potential to illuminate the relationship between individual behaviors and system dynamics. Figure 1.
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