Complexity theories represent a research approach that makes philosophical assumptions of the emerging worldview, which include holism, perspective observation, non-linearity, synchronicity, mutual causation, relationship as unit of analysis, etc. (Dent, 1999). Although complexity theories are being used by an increasing number of academics to better understand organizations, innovation, change and learning, among other aspects, the application of these ideas inspired by the physical sciences to the social world can oftentimes be controversial. While some authors draw analogies between organizations and organisms (Gregersen and Sailer, 1993; Stacey, 1995; Thiétart and Forgues, 1995), others have serious doubts about its applicability, because human systems are not like other systems in the physical world (Johnson and Burton, 1994). In contrast, Tsoukas (1998) understands that both views are missing the point, because one cannot be certain whether one has captured the nature of an object of study. He proposes applying these ideas to organizations and seeing what the consequences might be (Tsoukas, 1998: 305). Similarly, Houchin and MacLean (2005: 152) claim that the best use we can make of complexity theories in understanding organization development may be as a metaphor to give us new insights, rather than trying to search for common principles across a variety of very different systems (Tsoukas and Hatch, 2001). However, this metaphorical approach does not imply we should ignore the role played by emotions or politics, or the options available to individuals to interpret, adjust or break rules in human organizations. These specific characteristics of human organizations need to be considered in order to improve our understanding of them. This is precisely our approach in this paper: to obtain new insights from complexity theories for the study of OL.
Complexity is a comprehensive concept for a number of theories and ideas that are derived from scientific disciplines such as meteorology, biology, physics, chemistry and mathematics (Burnes, 2005). Therefore, a group of theories come together under the general designation of complexity research. As we mentioned above, papers that focus on these theories to advance our comprehension of organizations indicate a few terms or ideas that are assumed to be essential in their analysis (e.g. Houchin and MacLean, 2005). In our paper we essentially focus on two complexity concepts: self-organization (Kauffman, 1993; Gell-Mann, 1994); and implicate order (Bohm, 1980; Bohm and Peat, 2000). Below, we briefly describe each of these concepts, and explain why they are related to learning within organizations.
Dooley et al. (2003: 62) state that a basic assumption within complexity theories is that organizations can be viewed as complex adaptive systems (e.g. Gell-Mann, 1994; Coleman, 1999; Anderson, 1999; Axelrod and Cohen, 1999; Houchin and MacLean, 2005). These systems are composed of semiautonomous agents that seek to maximize fitness by adjusting interpretative and action-oriented schema that determine how they view and interact with other agents and the environment (Dooley et al., 2003). These systems are made up of heterogeneous agents that inter-relate with each other and with their surroundings, and are unlimited in their capabilities to adapt their behavior, based on their experience. Consequently, they are complex in that they are diverse and made up of multiple interconnected elements, and adaptive in that they have the capacity to change and learn from experience. Adaptability is a system’s capacity to adjust to changes in the environment without endangering its essential organization.
Complex adaptive systems are capable of anticipating the results of their actions, for which they develop schemas or models (Holland, 1995; Anderson, 1999; Stacey, 1996). Each agent’s behavior is dictated by a schema, a cognitive structure that determines what action the agent will take, given its perception of the environment (Anderson, 1999: 219). In organizational systems, agents might be individuals, groups, or a coalition of groups. Different agents may or may not have different schemas, and schemas may or may not evolve over time (Anderson, 1999). Gell-Mann (1994) argues that complex adaptive systems encode their environments into many schemas that compete against one another internally. Changes in agents’ schemas, interconnection among agents, or the fitness function that agents employ produce different aggregate outcomes. Agents are partially connected to one another, so that the behavior of a particular agent depends on the behavior of some subset of all the agents in the system. Each agent observes and acts on local information only, derived from those other agents to which it is connected (Anderson, 1999).
Complex adaptive systems continuously self-organize (Anderson, 1999; Axelrod and Cohen, 1999). Self-organization is a process in which the internal organization of a system increases in complexity without being guided or managed by an outside source. No single program or agent completely determines the system’s behavior, which is rather unpredictable and uncontrollable (Goodwin, 1994). Pattern and regularity emerge without the intervention of a central controller. Self-organization is a natural consequence of interactions between simple agents (Anderson, 1999). Although emergence is unpredictable and uncontrollable, Griffin et al. (1998: 321) underline that it is intelligible, as we can perceive the pattern of its evolution. Consequently, not just anything could happen: there is an immanent rationale as to how the system unfolds a generative process at work that goes beyond the correlation of causes and effects. Although it is not possible to determine or control results, according to the literature it is possible to help self-organization to happen, by facilitating the highest effective complexity or the edge of chaos.