Interactive complexity

Complex systems have intricate interdependencies among their various parts and many variables operating at the same time²² (Longstaff, 2003, p. 15). They exhibit cause and effect that are distinct in time and show emergent behavior. They are typically nonlinear, with surprising compounding effects as a result.²³ Because of their non-linear character, “adding an element that can be duplicated to the system may cause a shift in the total system that is much greater than the amount added”. As a consequence, complexity can emerge suddenly: “as soon as you get more than a few

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layers and more than a few variables the complexity starts to go up” (Longstaff, 2003, p. 23-24). The measure of interactive complexity is the number of ways in which components of the system can interact, which in its turn depends on the number of variables in the system, the number of relationships between the variables and the number of feedback loops through which the variables interact (Cooke & Rohleder, 2006, p. 215). Moreover, it is argued that a complex system exhibits complex interactions when it has unfamiliar, unplanned, or unexpected sequences, which are not visible or not immediately comprehensible. According to Perrow (1999), systems are either

‘complex’ or ‘linear’. Table 2.1 shows the characteristics of both typologies. Although Perrow defines complexity by illustration rather than by rigorous definition, he illustrates his concept of interactive complexity with examples (Berniker & Wolf, 2001, p. 17-18). While not rigorous, the concept of complexity clearly conveys the idea that it is concerned with interactivity and not simply the number of parts, components or subsystems present (Thompson, 1967).

Complex Linear

Component proximity Spatial segregation (of components and subsystems)

Common-mode connections Dedicated connections

Interconnected subsystems Segregated subsystems

Limited substitutions Easy substitutions

Unfamiliar or unintended feedback loops Few feedback loops

Multiple and interacting controls Single purpose regulating controls

Indirect or inferential sources of information Direct information Limited understanding of the processes

involved

Extensive understanding of process technology.

Table 2.1 - Complex vs. linear systems (Perrow, 1999, p. 85-86;Berniker & Wolf, 2001)

Whereas linear systems are far more easy to manage, complex systems confront us with the problem of ‘managing the unexpected’ (Weick & Sutcliffe, 2001) due to the unpredictable nature of their interactions. These interactions are called ‘baffling’ (Perrow, 1999) because they surprise organization members; they are not intended to exist, but yet do exist. “These [baffling interactions]

represent interactions that were not in our original design of the world and interactions that we, as operators could not anticipate or guard against. What distinguishes these interactions is that they were not designed into the system by anybody; no one intended them to be linked. They baffle us because we acted in term of our own designs of a world that we expected to exist-but the world was different” (Perrow, 1999)²⁴. As such, these interactions are very difficult to anticipate.

Conceptually, complexity is the condition of a system, situation, or organization that is integrated with some degree of order but has too many elements and relationships to understand in simple analytic or logical ways. Examples are a team of people, a city, or an ant colony. Internal complexity,

from an organization’s viewpoint, is its ability to exhibit or contain a large number of states or behaviors (Bennet & Bennet, 2004, p. 290). It is measured by its variety, the number of possible states that the system can have. An organization of high variety has a large number of options and choices of actions it can take to adjust itself internally or when responding to or influencing its environment. If its variety becomes too high, the organization may become chaotic, with no coherence of thought or action (Bennet & Bennet, 2004, p. 290).

In the Perrowian (technical) view, interdependence or coupling is the degree to which organization components – whether they are applications, functions, departments or individuals – depend on each other. Loose coupling means that the components can operate independently from one another. Tight coupling means a continuous interchange of information, goods or services. The degree of coupling can be inherent to the organization’s nature, but it can also be the result of management decisions. Such is the case where organizations are forced to operate with tighter coupling as a result of cost cutting measures, meaning that tight-coupling may be a managerial decision based upon budgetary stress or profit targets.

2.2 Relevance

The relevance of Perrow’s work has often been downplayed, because most accidents are not caused by the combination of complex interactions and tight coupling. It has even been suggested that some accidents are not caused by tight coupling but by loose coupling (Weick, 2001b). But interactive complexity and tight coupling are strong concepts, independent of each other (Rijpma, 2003, p. 37). This is an explanation why NAT has had (and still has) a tremendous influence on organizational reliability studies. With over 2800 citations (scholar.google.com) it definitely is an all-time citation favorite. We believe its popularity can be explained by (amongst others) its broad applicability, a high face validity, its theory foundation and the spirit of the times.

The dimensions of coupling and complexity, along which the risk bearing of an industry, organization or technology is determined, are universally applicable over sectors, cultures, situations. The degree of interactive complexity and coupling, although hard to objectively measure, is consciously and unconsciously omnipresent, be it in interpersonal relations or conceptual notions. In theory, it is feasible to determine whether a situation is tightly or loosely coupled, or that the interactions between system components are linear or complex.

The combination of the dimensions of interactive complexity and degree of coupling has great potential of explaining (un)reliability. Separately, these dimensions only in part indicate why some type of organization is more prone to accident. Complex organizations are more than linear organizations, and tightly coupled organizations are more complex than loosely coupled organizations. However, some complex organizations seem to be indifferent to calamity whereas others are not. The same goes for some tightly coupled organizations. Only those organizations in the complex-tightly coupled quadrant are the most danger-prone.

Another aspect of NAT that is important for the development of the discipline as a whole is that Perrow used Cohen, March and Olsen’s (1972) provocative `garbage can’ theory to understand the safety risks in highly hazardous systems (Smart et al., 2003, p. 737). More in particular he drew on one of its two components, namely the emergence of unclear technology (the other two being problematic preferences and fluid participation). The high appeal of his Normal Accidents Theory is

to a large extent due to the popularizing of the Garbage Can metaphor in the 1970s and its challenging the rational view of organizations as depicted by the classical and early modernist tradition in organization theory (Smart et al., 2003, p. 737). Thus, indirectly, the tremendous appeal and face validity of Garbage Can theorizing has laid the foundation for work like ours.

We live in an era where complexity and interconnectedness are part of our daily lives and get a lot of attention. It is commonly assumed that the evolution of both dimensions proposed by NAT are positively inclined and fast-paced. The NAT message of being risk-averse where nuclear technology, nuclear weapons and other potentially dangerous industries are concerned, finds a fertile soil in the spirit of our times. Therefore, the situations it addresses are easily recognized and what the theory proposes is eagerly acknowledged.

Despite their appeal, these characteristics in themselves are not sufficient as a validation of NAT’s hypotheses. A rigorous gathering of data is needed. Unfortunately, there is little empirical evidence that can be found to validate NAT beyond what we have described above. This becomes clear when having a closer look at what a NAT authority like Scott Sagan (2004, p. 16) puts forward as part of the theory’s rationale when referring to scholars having applied or further developed the ideas to a much wider range of organizational, personal, and national activities. These include (to give just a partial list), hospital emergency room procedures (Paté-Cornell, Lakats, Murphy, & Gaba, 1997), the origins of the Franco-Prussian War (Nickles, 1999) and U.S. Air Force friendly fire incidents (Snook, 2002). Apart from the last study, literature only addresses NAT superficially. A more recent thorough and successful validation of NAT has been provided by Wolf and Berniker (2008) who tested NAT’s application to petroleum refining by linking the occurrence of incidents and accidents to the structure of the organization and its technology.

3 High Reliability Theory (HRT)

Despite NAT’s prophecy of doom, some organizations seem to cope really well with errors.

Moreover, they do so over a very long time. Researchers from the University of California, at Berkeley (Rochlin, La Porte, & Roberts, 1987), later followed by their colleagues from Michigan State University at Ann Arbor (Weick, 1987), started calling this kind of organizations ‘high reliability organizations’ (HROs). The collection of their work has become known as High Reliability Theory (HRT). High Reliability Theory is a theory²⁵ on how a high degree of reliability can be achieved. An organization in which this theory is practiced is called a High Reliability Organization (HRO).²⁶ HROs are organizations where the risk of calamity is very high, but that yet are successful in their management. HRT disagrees with NAT in its assertion that a simultaneous centralization and decentralization is necessary but practically impossible. Research in HROs argues that such is feasible because of structural and contextual idiosyncrasies, explored in the following subsections. We start by providing a definition of HRT (3.1), a depiction from the perspective of the two main schools of thought (3.2) and an overview of research in the HRO field (3.3).