Complexity Theory

Complexity theory is a relatively new intellectual endeavor that has its roots in the development and growth of systems theory. It is not a single body of knowledge in the sense that, for example, economics is. Rather, the theory has developed as an interdisciplinary study of systemic behavior. It includes fields such as economics, meteorology, biology, chemistry, and geology, as well as Kenneth Boulding’s idea of “multisexual” disciplines, such as biochemistry and behavioral economics. The hierarchical classification of different types of systems proposed by Boulding in 1956 implied that systems from different disciplines can share characteristics and thus be seen as “similar,” even though the language and units of measure are quite different. The emerging recognition that there were interesting resemblances, overlaps, and behaviors among systems in these diverse disciplines resulted in the establishment of the Santa Fe Institute in 1984 to formally explore the nature of complex systems behavior.

Social systems and institutions, including modern business organizations, are classic examples of complex systems. Applying insights from complexity theory for understanding and influencing these entities typically results in recommendations that differ considerably from those proposed by perspectives that are anchored in traditional disciplines. This entry examines the central concepts of complexity theory and its ties to corporate reputation.

Central Concepts

Complexity derives from two main system attributes: (1) the number of interacting components in the system, called detail complexity, and (2) time, or dynamic complexity. The nature of the interactions among the network of system components also contributes to the system’s degree of complexity. These interactions are reciprocal, or feedback, relationships, where an agent’s actions taken at one point in time have consequences for that agent at a future time. Relationships can be further classified as either linear or nonlinear. Although most interactions in the real world are nonlinear, linear interactions are significantly simpler to represent mathematically. Linearity, therefore, serves as the default assumption in many formal models of complex social systems, especially in economics. However, since most of reality is nonlinear, there is a cost associated with ignoring this aspect.

Attributes of both detail complexity and dynamic complexity need to be present at meaningful levels for the system to be truly complex. For example, a system with many interacting component parts satisfies the first condition. However, if the dynamic behavior is very stable, then the overall conclusion is that the system is not a complex system in the sense of complexity theory. Conversely, a very simple system, such as a two-jointed pendulum, once set into motion, traces out a very complex path over time. This would qualify as a complex system. Systems are “complex” in that they comprise a number of interacting components that exhibit interesting dynamic behavior because of these interactions.

From studies of complex systems in many different areas, several generic defining characteristics have been identified. First, the presence of feedback loops connecting the system elements generates behaviors that either amplify (positive feedback) or dampen (negative feedback) the effects of actions imposed on the system. The elements of the system interact strongly, and as a result, their interactions can result in many different observed outputs. The system’s behavior may be very sensitive to the initial conditions at the time the system is subjected to an input. Under some conditions, the system’s behavior may become chaotic. But in all cases, there is the possibility of a multitude of states of varying stability. A further consequence of the potential for extreme behavior is that the system’s observed behavioral outcomes do not necessarily follow a Gaussian distribution. Consequently, relying on traditional statistical tools for prediction may not be warranted. As modern business organizations are an important class of nonlinear dynamic systems, this has implications for strategic management practice.

Complexity Theory and Reputation

Reputation is an intangible organizational asset or resource and, as such, plays an important role in the organization’s value-creating activities. Considerable effort goes into measuring and managing a firm’s reputation, primarily through regular reputation surveys of key stakeholders. These survey instruments are based on familiar operationalizations of various aspects of this elusive construct. However, one of the claimed main conclusions drawn from complexity theory is that the future is essentially unknowable. Reputation is a multidimensional construct that combines elements from a variety of organizational subsystems, including finance, human relations, communications, and production. The interactions among these systems are tight and dynamic; the nature of their interactions is shrouded in causal ambiguity. Management, in principle, has responsibility for these organizational activities, but in reality, the emergence of reputation is not well understood due to the complexity of the issue.

The implications of complexity theory for management are unclear and, to a degree, controversial, but there are some insights that can be identified for reputation management. Primarily, these relate to the need for a language that enables managers to look across organizational boundaries and coordinate better the many separate, but interrelated, activities that contribute to high performance in all aspects of organizational functioning. Over time, this will result in the development of a solid reputation.

Just as an organizational system can exist in many metastable states, management needs to be adaptable to the requirements of the dominant state. This line of thinking leads to a distinction between ordinary and extraordinary management. Ordinary management is appropriate for the regular, day-to-day problem-solving and decision-making activities that are representative of the rationalistic management paradigm. The objective is stability and cost-effective performance. Extraordinary management is seen as the appropriate style for organizational systems undergoing significant change.

A fundamental distinction between the two management forms is that ordinary management operates in a single-loop learning mode. The underlying mental models remain essentially fixed, and organizational change is incremental within the limits of the mental model. Extraordinary management is a double-loop learning process. In this case, the reigning mental models are questioned, challenged, and modified in the process of exploring radically different ways of operating.

Effective management in an organizational system is fundamentally a learning process. Management in a learning perspective, which includes reputation management, requires that managers be sensitive to the conditions of the organization. Applying systems thinking methodologies and tools will enable them to clarify their understanding of how reputation emerges from organizational activities, as well as their mental models of these understandings. Reputation is formed in the minds of the organization’s stakeholders through psychological and sociological processes that are not completely understood. As such, reputation can be seen as an element of complexity that both is caused by the complexity of the organization’s actions and affects the organization’s actions in a nonlinear feedback process.