Managing Static and Dynamic Complexity in Change Projects.

Faculty of Business and Economics 1 Luuk Bolijn

Managing Static and Dynamic Complexity in Change Projects.

Complex Projects are difficult to manage because they suffer from uncertainty. Static complexity consists of many distinct factors, which are not non-linearly related. This causes uncertainty because of the amount of resources required to analyze all factors, and because the human brain cannot incorporate that many factors. Dynamic complexity consists of non-linear relationships. This enables it to adapt during its process, and thus is impossible to analyze before the system is active. Complexity can be managed before the project starts by reducing or transferring complexity, and by enhancing the project management’s ability to analyze complexity. During the project, an incremental approach can be taken to postpone complexity, to when more information is available. Additionally, complexity can be used to set up project which is able to actively adapt to new information.

Keywords:

Static Complexity, Dynamic Complexity, Project, Uncertainty, Unpredictability Introduction.

Complexity theory, or theories, present a significant shift in how processes are perceived. The classical, Newtonian view of a process is linear: one plus two equals three; x determines y. Complexity theory addresses the fact that many processes are not quite that simple. As a field, organizational science is still quite based on Newtonian principles. Elaborate models predicting financial products or situations still use linear models. Though, over time these models consistently fail to produce successful forecasts (Mainzer, 2009). However, organizational science is making forays into complexity theory (Anderson, 1999).

This article looks at the consequences of complexity for organizational change projects, and how complex organizational change projects should be managed. Based on their low success rate (Brodbeck, 2002. Burnes, 2009. Clarke, 1999. Harung et al. 1999. Huczynski and Buchanan, 2001), change projects will likely benefit from an expanded view on how to manage them. This article is based on the premise of two kinds of complexity (Wood, 1986): static and dynamic complexity. It is thought that static complexity is complex

Faculty of Business and Economics 2 Luuk Bolijn natural sciences. Secondly, methods of

coping with complexity will be presented.

An in depth view of Complexity Theories.

The first notion of complexity theory originated in 1934, when Bachelard first stated that simple ideas cannot be separated from their whole plethora of relations. Roughly a decade later, new views regarding complexity were beginning to gain a foothold in the scientific community (Weaver, 1948). This was due to the discovery of disordered phenomena, such as the third law of thermodynamics or discontinuity of quantum mechanics. This ‘disorganized complexity’ meant that complex processes could no longer be reduced to be understood in smaller parts. ‘Organized complexity’ was also defined, and found in other fields than physics. Organized complex processes were somewhat halfway between the classical, linear model and disorganized complexity. They are not disorganized, but they do consist of a large amount of factors functioning simultaneously.

In these early ideas, there is a hint of a dynamic and static dichotomy. From the discoveries of disorganized processes comes the first notion that processes are not always static and linear. Processes which were deemed to be constant, maybe subject to change.

However, this view is still limited to the fields of chemistry and physics. Organizational phenomena are deemed to be organized.

The rise of complexity as a topic is paralleled by the rise of computing devises. After the Second World War, computing devises improved due to the developments made in communication and encryption research. Additionally, military development focused on operation analysis or research.

Computing devises were developed with an aim at information processing (Shannon and Weaver, 1963), more specifically, the conversion of information along electromechanical devices. This new field of research: Automata theories, presented an abstract view of organized complexity. The complexity in Automata theories is organized, since the processed information was deemed to fit a relatively classical input-output format (Nelson, 1967). The work on Automata theories was supplemented by the research on cybernetics. Cybernetic research was initiated by the US Army in order to increase the aiming capabilities of anti-aircraft guns Cybernetic research attempted to analyse, and create, the control and communication of systems (McCulloch and Pitts, 1943). The overall goal was to create ‘self-governance’ (Wiener, 1948, 1961). Systems which were capable of adapting based on input. This gave rise to the concept of feedback. Although not recognized immediately, feedback would prove elemental in complexity theories.

The developments of operation research addressed the notion that complex processes cannot be reduced to fit a single area of expertise, but should be viewed holistically. During the Second World War the British military used ‘mixed teams’ to deal with complex combat situations. Teams consisting of mathematicians, physicists, engineers, physiologists, biochemists and psychologists. These teams pooled their knowledge to solve complex, multidimensional problems (Beer, 1959. Churchmann, Ackoff and Arnoff, 1957).

Faculty of Business and Economics 3 Luuk Bolijn relationships which would make processes

capable of ‘thinking’. This seems akin to dynamism; the ability to adapt. The work on self-governing systems aimed to create a system which was capable of moving beyond its own rules. The system was required to adapt to a situation based on the feedback it received. This ability to adapt makes the work on automata and cybernetics the first insight into how a complex system can change.

Secondly, a more holistic view of processes was taken. This takes into account all the different components which could feature in a process. Organizing a successful military operation is not bound to military factors. Psychology, sociology, geography and meteorology for example, all play a part since the operation concerns both humans and a physical location. Though, this view does not take any adaptive ability into account.

Up until 1960, there still is no explicit distinction between a static or dynamic view of complexity. However, the two directions do differ in their ‘dynamism’. The work performed on automata and cybernetics clearly moves towards an adaptive and thus dynamic definition of complexity. The holistic view does not take this into account, and thus is static. Following these early developments, Complexity theory made huge developments up until roughly 1980. The development of computing systems continued. Additionally, scientific developments made in biology, chemistry, physics and organization theory sparked the development of complexity theory. Developments in computer science gave rise to several mathematical or algorithmic versions of complexity (Ashby, 1956. Marcus, 1977). The size of the data computational systems needed to cope with gave rise to descriptive, generative

and computational complexity versions of algorithmic complexity (Knuth, 1968). These definitions describe the complexity of algorithms according to the size of the code and the amount of time required to devise and execute the code (Rescher, 1998).

In organization science, attempts were made to combine the holistic view of operation research and the adaptive view of cybernetics. The development of decision making processes for corporations were heavily influenced by game theory and heuristic problem solving (Simon, 1947, Simon & Newell, 1958). These developments in turn, led to a new body of science: Artificial Intelligence. Cybernetics also developed. Cybernetics research shifted towards the creation of complex organizations as autonomous systems (Von Neumann, 1966). Additionally, these systems would have the ability to evolve based on both their environment and internal relations (Ashby, 1956. Von Foerster, 1960, 1996. Atlan, 1972). The field of cybernetics expanded the search for self-organization in systems

Parallel to the rise of self-organization ran the discovery of non-linearity in physics and chemistry. The discovery of ‘dissipative structures’ sparked a new view in physics. Dissipative structures allowed for both order and disorder in thermodynamics (Prigogine and Stengers, 1984). This inspired the notion of ‘catastrophe theory’ and ‘chaos theory’; non-linear relationships made outcomes so sensitive to initial conditions that they became unpredictable (Thom, 1975). This was then supplemented with the concept of fractals. Fractals’ self-similarity made for a certain order in disorderly seeming natural structures (Mandelbrot, 1983. Mainzer, 2007).

Faculty of Business and Economics 4 Luuk Bolijn touched the field of biology. Theories to

explain the emergence of life, evolution and the emergence of cognition were adapted according to the presence of disorder and non-linearity (Monod, 1972. Dawkins, 1989. Gould and Eldredge, 1977).

Inspired by the rise of cybernetics, system theory re-emerged. Based on the idea that anything is describable along the same principles, systems theorists set out to find a ‘general systems theory’ (Von Bertalanffy, 1951). However, in the 1970s the science of systems split along two distinct paths. The first, still holding that anything is explainable, aimed to reduce complexity to manageable parts (Forrester, 1961. Churchman, 1968). The second path diverged from this idea. They took to a more constructivist view, and recognized the importance of the relationship between the observer and the phenomenon.

During the period between roughly 1950 and the 1980s, complexity theories moved from exploration to full-fledged, supported theories. There is also a clear move towards a dynamic view of complexity. The ability of the system to adapt, evolve, or otherwise change is the base for developments in natural sciences as well as organization science.

Complexity theories continue to develop along the same lines as the previous period. Artificial Intelligence and Evolutionary Biology have become the major fields for the development of complexity theories. These developments are still dependent on the application of complexity theory on a subject. Complexity theory has also been researched independently of its applications. Currently, three different streams of complexity theories exist: Firstly, there is algorithmic complexity (Barnes and Duncan, 1991. Chaitin, 1992.

Pickles, 1995. Traub, 1996. Eastmen, 1999). Algorithmic complexity represents the body of work which aims to represent the behaviour of systems in an algorithm. This is mostly used in information theory, or in the modelling of natural phenomena’s (most notably biodiversity). This view of complexity is in actuality the mathematical application of the other two different streams. However, the size, difficulty and importance of the field warrant its own defined stream.

Secondly, there is the deterministic view of complexity. This stream aims to explain how systems produce certain results. More specifically, how feedback, sensitivity to initial conditions, bifurcation and strange attractors produce unpredictable results. This stream follows up on the work on chaos theory (Gleick, 1987. Lorenz, 1995), dissipative structures (Prigogine, 1997) and fractals (Mandelbrot, 1977). The principle concept of the deterministic stream is that through certain machinations the outcome of a process in unpredictable. A feedback loop, for example, can increase the slightest initial conditions to huge proportions: the popular butterfly effect. Another example is bifurcation. Bifurcation is the sudden jump in a system from one attractor to another (Feigenbaum, 1980). This means that a very small change in a variable could radically move a cycle from boom to bust (Nijkamp and Reggiani, 1990. Byrne, 1997).

Faculty of Business and Economics 5 Luuk Bolijn deemed unpredictable due to the inability

to acquire or process knowledge to such a degree (Reisch, 2001. Peterson and Flanders, 2002. Miller, 1956. Cowan, 2001). This criticism is purely philosophical. Many developments in this stream of complexity are due to its application in natural and social sciences. For the purpose of any applied version of deterministic complexity, it produces unpredictable results.

The last stream is that of aggregate complexity, or Complex Adaptive Systems (CAS), as it is more commonly known. Complex Adaptive Systems present a very comprehensive view emerged from the Santa Fe Institute in New Mexico: Complex Adaptive Systems. This new view is based on the works of Holland (1992), Kaufmann (1993), Bak (Bak & Chen, 1991) and Wolfram (2001). The CAS view provides a good overview of what concepts can be influenced in order to get certain results. The CAS view states that, on any level of analysis, order emerges from the interactions at lower levels of aggregations. Thus, aspects at lower levels can be influenced in order to change at higher levels. A biological nutritional cycle for example, is based on intake and output of several different types of wildlife; this is a very high level of aggregation. On a lower level the nutritional cycles of independent animals come into play. Influencing these, changes the input on the higher level cycle.

Generally, four aspects feature in the CAS view. Firstly, there are agents. These agents produce a certain outcome at a lower level than the level of analysis. These agents act according to certain rules, which may be flexible. So, in the case of an organization, these agents are individuals, or groups of individuals. The rules would then be the neural network of a person. Secondly, these agents are

connected by feedback-loops. This seems very similar to self-organization as produced by cybernetics. However, CAS stipulates that self-organization cannot be achieved fully without the importation of outside energy. Thus, a CAS has the capabilities to self-organize, but requires outside stimulations. Thirdly, agents co evolve. Agents adapt to their environment in order to achieve a goal. Their environment is dependent on the choices of other agents, and thus constantly in motion (due to the feedback-loops). The system will evolve to the edge of chaos, a constantly shifting equilibrium. Finally, this evolution is also influenced by the entry, exit and transformation of agents. These will cause new relations, and thus new co evolution.

This view has been appreciated by the field of organization science because it gives a relatively clear overview of the different interaction in play. Thus making it easier to reduce a complex system in manageable parts.

Faculty of Business and Economics 6 Luuk Bolijn complexity is the number of hierarchical

levels. Horizontal complexity relates to the number of departments, and spatial complexity is the number of different geographical locations (Daft, 1992). Complexity is also defined as simply the number of distinct aspects that need to be handled simultaneously (Scott, 1992). Additionally, definitions which feature complexity as the result of uncertainty also exist. In this case the complexity of a system depends on the number of aspects that are uncertain, and to which degree they are uncertain (Little, 2005).

These definitions of complexity are all minimally related to the general body of complexity theories presented earlier. The definitions focus singularly on the components of a system. Though they do take the holistic aspect of complexity into account, non-linearity has not been accounted for. Therefore, these definitions have received criticism from the field of complexity theory (Plowman and Duchon, 2008. Uhl-Biehn and Marion, 2009). Stating that these systems are not complex, but complicated. These definitions are said not to be complex, because they do not feature the ability to change based on non-linearity.

Although they might not be part of complexity theory, these definitions are part of organization science, and thus need to be taken into account. Regardless whether or not they can be classified as complex theory, these definitions do present a case for static complexity.

Towards a working definition, and its consequences.

As has been shown, there is quite a large spectrum of complexity definitions. However, they can be roughly put into two bodies. The first is that of the “complexity theories”, this title refers to the body of

work which is typified by non-linear relationships, and thus unpredictability. The second body consists of the, for lack of a better term, ‘complication theories’. The second body thus consists of definitions which model holism, but in a linear fashion.

The first body stipulates the system’s ability to change. Some complication theories also incorporate change. Wood (1986), for example, highlights the fact the change outside of the system will change factors or relationships. However, complexity theories are characterized by the system’s ability to change itself. The two broad definitions are not mutually exclusive. A system can of course have a large number of different factors, and those factors can also relate to each other non-linearly. However, they do differ severely where the dichotomy static-dynamic is concerned. The complexity theories intrinsically imply dynamism. A system functioning along non-linearly lines, which is capable of changing itself, is by definition dynamic. Complication theories do not necessarily exclude or hamper change. However, when outside stimuli are ignored, they do present an adequate definition for static complexity. When a system is complex because of the number of distinct components, and relationship, but those components and relationships are unchanging, one could speak of static complexity.

Faculty of Business and Economics 7 Luuk Bolijn A Dynamically Complex System includes

one or more non-linear relationships. A Statically Complex System consists of a large number of different factors, which are linearly related.

These definitions of static and dynamic complexity are quite abstract. How do they influence organizational theory? They give an indication of how difficult, or impossible, it is to predict the outcome of a process. Management is purposeful; it attempts to achieve a goal. Therefore, the ability to set up a process to achieve a specific result is of major importance in the field of management. The vast bulk of organizational and management models show how one can achieve a certain goal. Complexity impedes one’s ability to devise a specific, fool-proof method. This is due to uncertainty which arises from both types of complexity.

Static complexity causes uncertainty because often there is too little time to assess every factor (Geraldi, 2009. Little, 2005. Xia and Lee, 2004, Wood, 1986). The summation 1+1, takes less time to solve than 23+45+598, for example. Management is bound by time, decisions need to be made at some point. When a process suffers from Static Complexity, there generally is too little time to account for all the factors. Thus, the process has not been fully analyzed, and the outcome will be uncertain. For example, if one has only a second to solve 23+45+598, it is quite conceivable that only the first part has been solved. At first glance, it might seem that spending a more time analyzing the system would be the answer. However, as of yet, organizational science cannot present clear-cut, fool proof methods for managing all that affects an organization. It is safe to assume that, despite extensive analysis and research,

the vast multitude of factors involved in organizations will always lead to some uncertainty.

Uncertainty stemming from dynamic complexity can be better categorized as unpredictability. The non-linear, adaptive nature of the system offers no possibility for a predictable outcome (Morton, 1988, Batterman, 1993. Manson, 2002. Werndl, 2009). Non-linearity and adaptive ability infers unpredictability upon a system because of two reasons. Firstly, the deterministic view explains how sensitivity to initial conditions, feedback, bifurcation etc. makes for unpredictable results. An unperceivable difference in the initial starting conditions of two simple playground swings for example, will lead to them swinging at a different pace. Secondly, the adaptive ability of a dynamically complex system means that the system cannot be fully known. When the process changes during its execution, all previous predictions are worthless. For example: A+1=B. Where A=B. This seems like an impossible equation, how can B be both A and A+1? Well it cannot. That is why the system has adapted. The outcome of the summation has changed the input. This is an example of a feedback loop: B feeds back on A. This means that the system is constantly changing. CAS provides a useful framework to further explain this. B is input on a high level. B+1, for example. This makes the equation A+1=B a lower level. Because B is constantly changing, B+1 cannot be solved.

Faculty of Business and Economics 8 Luuk Bolijn presented here dynamically complex. B

feeds back on A. However, the first part of the equation, 2+2, is linear. This will not change when A does. For the sake of space and readability, the equation only has two linear factors. Though, a hundred more could be added, yet they would still be linear. In that case, that part of the equation becomes statically complex; a large number of factor, which are linearly related. So the equation overall is dynamically complex. Parts however, are static. Thus, even though the system is dynamic overall, it does incorporate both types of complexity.

Articles concerned with the management of complex projects often use both types of complexity (Xia and Lee, 2003. Sommer and Loch 2004, Geraldi and Adlbrecht, 2007). In reality, projects might very well consist of both a large number of distinct variables, which can also be non-linearly related.

So what does this mean for the field of organizational change?

Styhre (2002) criticises change management models deemed as linear planning tools (Beer and Nohria, 2000, Kanter et al. 1992. Kotter, 1996). The case study he performed showed that the applied linear approach suffered heavily because of unanticipated factor influencing the change approach. The organization had more success when they adopted a more fluent approach towards their change initiative. This much heard criticism (Burnes, 2009. Mason, 2009) has lead to a new organizational model: an organization on the ‘edge of chaos’ (Waldrop, 1992). This concept was developed by the complexity theorist Chris Langton, and states that a Critical Adaptive System will move towards a state where there is just enough order within the system for it to no fall apart (Waldrop, 1992. Kauffman, 1995,

Matthews, 1999). Additionally a system on the edge of chaos will also be subject to large changes caused by small changes, similar to deterministic complexity. This will cause the system to evolve and self-organize. This ability to adapt seems to be the greatest asset for an organization, as it ensures the continuation of the system (Stacey, 1996).

The edge of chaos is seen as state which to aspire to, rather than a method specifically for coping with dynamic complexity. An organization on the edge of chaos will be able to adapt to circumstances in a natural, evolutionary way. Thus ensuring the organizations competitiveness. It represents the application of dynamic complexity in order to create a specific type of organization. Such an organization would be in a constant state of flux. The organization learns and adapts based on feedback loops between agents. These agents can be employees or departments, depending on the level of aggregation.

So, dynamic complexity can be seen as beneficial. This would suggest that rather than trying to cope with the uncertainties arising from complexity, the project should use dynamic complexity to adapt to uncertainties. Maximize the projects adaptive ability, while still ensuring enough structure to hold the project together. More on this subject will follow near the end of the article.

Managing complexity.

Faculty of Business and Economics 9 Luuk Bolijn the complexities of reality well. Though

completely representing complexity in model might be unachievable, these models and methods do offer ways to better understand complexity in a project. Other methods promote the management of complexity through a more flexible project organization. Decisions are postponed until more information is revealed.

Finally, there is the ‘edge of chaos’-approach. The edge of chaos often hailed as the answer to complexity in organizations. It attempts to actively be adaptive, like a dynamically complex system. Jafaari (2003) states that it is likely that an edge of chaos approach is best able to deal with complexity in projects. However, he also acknowledges a lack of significant literature or research to support such a statement.

Projects encounter complexity in three ways. Firstly, the project’s subject can be complex. Secondly, the project itself may be complex. A large number of project member for example, can make a project more statically complex. A lot of feedback which is incorporated will make a project dynamically complex. A project is part of a larger organization, and this influences the project as well. The complexity of the project itself can be increased when there are more links between the project and the organization. More links means that more factors are involved, and thus the static complexity will increase. If those links are non-linear, the dynamic complexity will be increased. Lastly, both the projects subject and the project itself can of course be complex. This is a likely scenario when the project’s subject is complex. The uncertainties of the subject will result in uncertainties for the project organizations. If the scope of the subject is uncertain for example, it is harder to

determine how many personnel the project should utilize.

No clear definition for a change project exists. However, organizational change is usually attempted by one-off projects (Styhre, 2002. Gareis, 2010). Though, as change projects attempt mobilize, they are inherently subject to uncertainty, and likely non-linearity. As it is impossible to completely predict what reaction change initiatives will cause, a completely linear project execution is unlikely to occur. Thus change projects are likely to always include an element on dynamic complexity. Of course

Pre-project: Complexity Analysis and Management.

As the previous chapter has show, it is difficult at best to completely analyze complexity before the system is active. Thus completely managing a complex project using traditional planning and structuring tools like work breakdown structures or critical chain management are very unlikely to be successful (Thomke and Reinertsen, 1998).

Faculty of Business and Economics 10 Luuk Bolijn complexity before the project

commences.

Xia and Lee (2003) split complexity in a type; structural and dynamic, and an area where they occur: organizational and technological. Organizational and technological complexity represents the complexities of the subject (technological) and that of the organization of project (organizational). These two categories encompass all factors which might influence the project. However, these factor may be subject to change when the project commences (dynamic), or they may not (structural). This yields four dimensions of complexity: structural-organizational, structural-technological, dynamic-organizational and dynamic-technological.

Dynamic-organizational complexity, for example, leads to weaker communication and stress. Dynamic-organizational complexity could be due to ill-defined or lack of attribution of project roles, or uncertainty concerning the projects position within the organization as a whole. Another example: ill defined technological-structural complexity leads to an insufficient budget, as the scope of the project has not been properly defined (Cilmil and Marshall, 2005). Geraldi and Adlbrecht (2007) present a different framework. They divide complexity into three types: complexity of faith, fact and interaction. Complexity of faith is uncertainty, usually caused by the uniqueness or unprecedented nature of the subject. Complexity of fact is the number of distinct elements in the project. Complexity of interaction is found in the interaction between people or organizations. Politics and multiple cultures for example, cause complexity of interaction. These three types are then supplemented (Geraldi, 2009), with Xia and Lee’s (2003) areas: organizational and technological, and the dynamism

(Remington and Pollack, 2007. Ribbers and Schoo, 2002) and pace (Shenhar and Dvir, 2007). Dynamism is the degree of change occurring during the project, and pace the speed of which the project should take place. In addition to creating awareness of complexity, elements inherent to project which produce complexity can be recognized. For example, project pace will almost always increase the complexity of the other categories (Geraldi, 2009). After the complexity of a project has been mapped, several methods exist to reduce the complexity which has been identified (Gottfredson and Aspinall, 2005. Schnedl and Schweizer, 2000). Firstly, static complexity can be reduced by pooling or removing factors. In the example of a simple summation; 12+8-15, can be reduced to 20-15.

Faculty of Business and Economics 11 Luuk Bolijn too much or do things too intricately. This

leads to complexity which might not even be beneficial. When complexity in the project exists by choice, it is best to review if it really adds value.

Next to reducing the complexity of the project, the ‘complexity’ of the project management can be increased. (Schwaniger, 2001, Gandolfi, 2010). This means that the project team increases its ability to cope with complexity. Firstly, according to Ashby’s law of requisite variety: only variety can destroy variety (Ashby, 1956). This means that the project management should incorporate a large degree of diverse knowledge. The easiest way to accomplish this is by letting team members participate in the decision making progress (Gandolfini, 2010. Ollauson and Berggren, 2010). However, sometimes the team additional members or training. Secondly, the way the project team view complexity can be refined. Richardson (2008) promotes that project management takes a deeper, philosophical, view of the project than simple causality. Richardson states that often, people determine causality in a single order fashion (A causes B) and only by what their senses perceive. Green (2004) is another proponent of a specific method of analyzing complexity. She found that the specific ‘cognitive complexity’ of the project leader influences a projects success. Cognitive complexity is divided into cognitive differentiation: the ability to split information into smaller units, and cognitive integration: the ability to combine smaller units into a whole. Green found that project leaders with a disposition for cognitive integration perform better. This supports the claims that complexity should not be reduced. Additionally, the previous chapter described other limitation of human perception and rationality. Thus,

philosophical and scientific reasoning are promoted to better reach an understanding of complex systems. Neo reductionism attempts to discover underlying causality by searching for the rules and laws behind complex systems (Richardson, 2008). Many of the deterministic complexity theories are based on such rules. Models such as agent-based simulations (Monostroni, 2003) and genetic algorithm are very advanced complex systems devised by humans based on such rules and laws. However, such systems are relatively novel, and devising and running such systems requires enormous amounts of resources. Moreover, they are still bound by human rationality, and thus prone to misrepresentation.

All the above techniques require project management to rationally analyze complexity. However, cognitive research shows that the rationality of the human brain is limited.

Estimated is that humans can cope with seven, plus or minus two depending on the person’s ability, components (Miller, 1956). Some scholars argue that the number may even be as low as four different components (Cowan, 2001). Whatever the number is, it is clear that the perception of complex systems is limited.

Faculty of Business and Economics 12 Luuk Bolijn old assumptions (Wack 1985, Johnson

1987). The availability heuristic is an example of how inappropriate frameworks arise. The availability heuristic states that people tend to use only the data readily available to them, (Tversky & Kahneman, 1974). Secondly, people have a tendency to see patterns in randomness (Ayton, Hunt, & Wright, 1989. Files and Goodwin, 2007. Gilovich, Vallone, & Tversky, 1985), and assign intention when there is none (Barrett and Hankes Johnson, 2003. Boyer, 2003. Castelli et al. 2000. Michotte, 1963). Overall, there exists an urge to assign certain causality. This impedes the analysis of complex processes. Due to the unobvious nature of the process, it is highly susceptible to bias, since there are no obvious answers. Thus, being aware of these limitations and biases, and therefore, limiting their effect on the analysis. This will enhance the pre-project analysis.

However, there is another side to the limitation of the human brain. The limitations to rational data analysis are part of an ancient survival mechanism. The human brain tends to automatically reduce information in order to speed up decision making. This helps quick decision making in high risk and high paced activities. When here is little time to assess a situation rationally, a form of pre-rational or pre-social awareness take precedence. This can be defined as intuition or instinct. (Reber, 1995. Lyng, 2005, 2008). Intuition can lead to unwarranted biases and limitations, as described previously. However, cognitive research has shown that intuition has quite a high success rate when used as a decision making heuristic.

Zinn (2008) proposes to use intuition in complex situations.

Intuition is described as tacit knowledge, innate knowledge or a pre-conscious awareness of the future (Reber 1995. Lyng, 2005, 2008). Intuition is honed by experience (Klein, 1998. Benner, 2001). Patterns in complex situations become more obvious.

Experts in cognitive science argue that intuition has developed to cope with the limited capacity of the human mind (Chase et al. 1998, Gigerenzer 2007). Intuition represents short cuts to recognizing relevant information. Research has shown that the recognition heuristic, the method of deciding based on the degree of recognition of options, is a very successful heuristic. It is often more successful than extensive rational methods such as multiple regression.

Gigerenzer for example, showed that, as a group, laymen who used limited information were more successful in predicting the performance of stock than experts who used a large amount of information. Successful stocks were picked based on the recognition heuristic. Well-known companies were selected over lesser, or not know companies. Thus, intuition might prove a more successful method of managing uncertainty or unpredictability than more advanced, rational methods.

Faculty of Business and Economics 13 Luuk Bolijn Although intuition flaws rational thought,

it does provide a honed heuristic for decision making in high-paced situations.

Managing Complexity at the right time.

Analyzing and assessing complexity before the project commences can certainly help the management of complexity. Some complexity might be removed, and the awareness of complexity has been raised. However, complexity cannot be completely managed by classical project management methods, which make elaborate plans. Due to non-linearity and the likelihood that not all factors are accounted for in the initial analysis, not all the information is available to produce a comprehensive project structure and planning. This is true when complexity exists in the project’s subject, on when the project is complex itself.

Because complexity cannot be completely managed before the project commences, complexity needs to be managed during the project run. There are two general, related, techniques to manage complexity. The first is to postpone dealing with complexity until more information is revealed (Thomke and Reinertsen, 1998. Gottfredson and Aspinall, 2005, Pundir et al. 2008). The second is to pursue several different approaches, than continuing with the one that works best (Thomke and Reinertsen, 1998. Loch et al. 2009).

Several methods exist which take an incremental approach towards decision making. Stage-gate models used in new product development process (Cooper, 1990) or the Delphi method (Linstone and Turoff, 2002), for example, use such an incremental approach. Such incremental approaches start with very general goals, which get refined as the project progresses. The stage-gate method can be combined with the trial-and-error approach promoted by Sommer and Loch (2004). A stage-gate models works by

developing the subject of the project during the stages. They then need to pass a gate in order to progress to the next stage. These gates become much more specific when the project matures, and thus, more of the project’s complexity is revealed. When the material does not fulfil the requirements for passing the gate, is goes through the stage again. Trial-and-error can prove very valuable in the case of complexity as often the intricacies of the system are revealed when it is in progress. Bannerjee et al. (2007) have developed a project management tool called the Design Structure Matrix (DSM), which specifically takes into account the need to perform an action multiple times. It offers some degree of technical support to a trial-and-error strategy.

Selectionism is the pursuit of several different approaches, and selecting the best one after trials (Loch et al. 2009). Selectionism offers flexibility by increasing the amount of options.

Both strategies can be combined. By pursuing multiple approaches incrementally, the project will incorporate a wealth of options.

Faculty of Business and Economics 14 Luuk Bolijn The most common argument against

these strategies is that they would be inefficient (Smith and Reinertsen, 1998. Gottfredson and Aspinall, 2005). However, in the case of complexity, less flexible project are often less efficient. Because the project cannot be adequately structured and planned, it will eventually need to be adapted. However, adapting an inflexible project requires a lot more time and resources than a flexible project. (Thomke and Reinertsen, 1998).

How to reach the Edge of Chaos.

The edge of chaos takes the management of complexity one step further by active adaptation, akin to a dynamically complex system. Such a project would not try to mitigate complexity, but rather take charge of it. Using a dynamically complex system to achieve the project goals, means that the project will take on an evolutionary character: the project’s subject gets refined based on feedback. This is a specific organization of a project. If it is used when the project’s subject is complex, it will be very similar to an incremental model. The heavy emphasis on feedback and adaptation replace incremental stages. Of course, the project is dynamically complex itself.

Working on the edge of chaos requires certain attributes of a project. Firstly, the project needs a loose end goal, or vision, which to achieve. Although the edge of chaos requires a distinct lack of extensive planning, a project cannot exist without a purpose. However, this purpose can be a lot more general or ambiguous than the planning of a classic project set-up. Secondly, the project needs to be flexible enough to adapt to the changing situation of a complex project. Thirdly, the project needs to have the requisite variety to cope

with the variety existent within the project. Finally, the project needs to have feedback loops. This is a very important structural element. The project adapts because of the feedback loops.

Faculty of Business and Economics 15 Luuk Bolijn Taplin, 2004. Wang et al. 2005. Ivory and

Alderman, 2005.). Cristenson and Walker (2004) state that project vision might be the key element in project success. Gary Chin (2004) has written an excellent book on how to set up a flexible project. In summary, a project should have a goal in mind, but be flexible in its way to achieve it.

In order to adapt to the changing elements in a complex project, the project needs to be flexible. Forcing a project to adhere to too much structure or planning, limits its ability to adapt. Davis et al. (2009) found this in their research towards optimal organizational structure in dynamic markets; a low amount of structure is better suited when dealing with unpredictability. Thus, it is important not to limit flexibility by enforcing too much structure. In the case of a project, not planning too far ahead can have a major effect on the project flexibility. When development cycles are short, much more accurate information will be available for planning (Smith and Reinertsen, 1998).

Additionally, project flexibility can be supported by certain types of employment flexibilities (Atkinson 1984. Reilly, 2002). A combination of functional flexibility and numerical flexibility (Kalleberg, 2001) will help to adjust project members to their tasks, as well as ensuring that the project team is capable of handling potential increases in workload.

However, simply decreasing the amount of structure, rules or planning will not necessarily make a project more flexible. Limiting inflexibility is a facilitative matter. A project on the edge of chaos needs to actively be flexible by seeking reasons to change.

Olausson and Berggren (2010) found in several case studies that a high degree of participation is beneficial to a projects success when operating under uncertain

and complicated circumstances. Project with a high amount of communication between team members, and between team members and team management proved to have a much better overview of the scope of the project. This meant that the project team had a much higher awareness of when adaptations were required, and when they were required. They propose that project teams meet often, preferably visible, to both reflect on the project and plan further progress. However, they do stress that these participative sessions be highly transparent to allow higher, organizational, management some form of control.

Participation helps immensely to achieve the requisite variety within a project. Since the ‘variety’ of the project is unpredictable, the requisite variety of the project team is unknown. The involvement of team members will increase the variety, especially in the case of a diverse team (Krefting, Kirby and Krzystofiak, 1997). This is identical to the measure taken in pre-project complexity management, since there is no reason not to heed the law of requisite variety in the case of the edge of chaos.

Faculty of Business and Economics 16 Luuk Bolijn should have multiple paths. This to ensure

proper functioning feedback loops, which do not breakdown with a single missing members, or lack depth of information. When proper feedback loops are in place, the project set-up is done. However, because a project on the edge of chaos is based on active adaptation, the project team needs to be supported in changing. Plsek, Lindberg and Zimmerman (1999) and Manning (1989) propose to use the anxiety that is created by a limited structure to make the organization eager to change and promote creativity. According to Baskin (1998), trust is also an requirement for successful instigating change. So both the desire to change, as well as the trust that the change will be successful and beneficial should be installed in the team members. This is also reflected in the importance of social skills for a project leader.

If the project suffers from static complexity, a set up akin to a Complex Adaptive System is preferable. The uncertainties arising from static complexity as a subject are handled through participation and feedback, similar to the measures presented in the previous chapters. Statically complex elements in the project itself present a problem for a project on the edge of chaos. Static complexity means that a lot of different factor come into play. This means more feedback lines. Too many unstructured feedback lines will severely hamper the efficiency of the project. Layering the project as a Complex Adaptive System will streamline the large amount of feedback lines. This consequently means that the project has several different sub-elements functioning as agents.

Conclusion.

Complexity is far from a coherent, unified theory. This, coupled with its origin in natural sciences, makes complexity a very broad and unwieldy concept for the field of organization science. However, two broad definitions for static and dynamic complexity can be defined for the purpose of organizational science. Static Complexity consists of a large number of different factors, which are linearly related. Dynamic Complexity includes one or more non-linear relationships. Static Complexity leads to uncertainty because often there is not enough time to assess every factor. Dynamic complexity leads to uncertainty because the system itself is adapting during its process. Additionally, the limitations of the human brain make complex system even more difficult to assess. However, in situations where time is limited, intuition does provide a useful tool decision making tool.

Complexity can be partly managed before the project commences by removing complexity. This can be done by enhancing the ability of project management to analyze complexity, or by reducing or transferring complexity. Emotions provide a great tool for reduction, and participation is a relatively easy way to enhance the analysis of complexity. However, since not all complexity can be analyzed at the project’s start, postponing complexity is an effective approach towards efficiently managing remaining complexity. An incremental approach to refining the project requirements helps to postpone complexity until more information is revealed.

Another option is to channel complexity. Using a loose structure and feedback loops, an evolutionary like process can take place.

Faculty of Business and Economics 17 Luuk Bolijn independent option, since complexity

Faculty of Business and Economics 18 Luuk Bolijn

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