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As a fundamental model for projects I want to use the notion of a Complex Adaptive System (CAS). In this post I will outline what this means; what are the properties of a CAS and how are they beneficial in our quest to search for answers to project problems?

Why CAS In The First Place?

I am the first to admit that my attention to the use of complex adaptive systems is largely triggered by its current popularity. It is a new and exciting concept that is getting more and more popular, and its associated attention, in almost every scientific discipline. It is this multi-disciplinary aspect in combination with new and exciting that sparked my interest.

The central thought that the interactions of a lot agents make up the behavior of the entire system, is similar to the opinions I hold when talking about projects: the interactions of all the individual stakeholders make up the behavior of the project. The fundamental idea resonates very well with my personal views on projects.

But only multiple agents that interact doesnt make a complex adaptive system:

“What distinguishes a CAS from a pure multi-agent system (MAS) is the focus on top-level properties and features like self-similarity, complexity, emergence and self-organization. A MAS is simply defined as a system composed of multiple, interacting agents. In CASs, the agents as well as the system are adaptive: the system is self-similar. A CAS is a complex, self-similar collectivity of interacting adaptive agents. Complex Adaptive Systems are characterized by a high degree of adaptive capacity, giving them resilience in the face of perturbation.” (source: Wikipedia)

These extra properties should bring something useful to the table. Otherwise we only introduce additional complexity (pun intended) without getting benefits in return.

So, we have:

• Self similarity
• Complexity
• Emergence
• Self-organization
• Adaptive capacity

I will turn to every aspect individually and discuss what it means and what benefits it brings in our modeling of projects.

(source image: Wikipedia.org)

Self similarity

“In mathematics, a self-similar object is exactly or approximately similar to a part of itself, e.g., the whole has the same shape as one or more of the parts.” (Source :Wikipedia)

For self-similarity to make sense in a project context, we have to include the following view:

• A project is a small organization
• An organization is a small society

Applying the concept of self-similarity this means that behavior and patterns of projects can be found in organizations and society. And vice versa, patterns seen in society can be found in projects. Every level of abstraction will have (near) identical properties, only the scale is different. If we look specifically to shapes of things (e.g. organizational, communication patterns) the concept of a fractal applies:

“… a fractal is “a rough or fragmented geometric shape that can be subdivided in parts, each of which is (at least approximately) a reduced-size copy of the whole” (Source: Wikipedia)

So, basically, self-similarity in geometric shapes. As emerging patterns are a very important part of interest in a CAS, and patterns are represented by shapes, the importance of fractals becomes clear:

“A fractal figure is a snapshot of a dynamic system at a stage of development. The snapshot is a clue to a dynamic process – a pattern of development.” (Source: Spectacle.org)

The self-similarity property allows is to apply concepts from society and general organizations directly to projects. If we are looking at why projects succeed or fail, we should find useful patterns in the discussions why societies in general succeed or fail.

Complexity

In a simple system you can easily see and predict how a system behaves. In a CAS you can absolutely forget that. The amount of variables that determine the path the system will take are immense and the slightest change in just one variable can set the system of in an entirely different direction (the famous butterfly-effect).

The good news is that using a CAS you can handle the vast amount of variables; the drawback is, the slightest deviation in start situation can change the course of a system dramatically, it is therefor indeterministic. This makes it useless for precise predictive powers about reality. But for our modest goal in modeling projects that is not a problem. The following quote sums it up perfectly:

“These authors re-emphasize in their conclusion their belief that such modeling is descriptively useful in explaining behavior, but that direct modeling is impossible. “Reviewing the selection of scenarios presented in this section, one summarizing remark should be made immediately. The extreme sensitivity of the dynamics to the initial conditions and numerical values prohibits any use… for predictive purposes….” (Source: Wcu.edu)

Emergence

“… emergence refers to the way complex systems and patterns, such as those that form a hurricane, arise out of a multiplicity of relatively simple interactions.” (Source: Wikipedia)

Self-organization

“Self-organization is a process in which the internal organization of a system, normally an open system, increases in complexity without being guided or managed by an outside source.” (Source: Wikipedia)

How the system organizes itself is part of the system. There is no need to have an external entity to take care of that. This makes the model of the CAS powerful, as it is self-contained in this respect. Even if you view projects as an artificially constructed system with dedicated structures, this doesnt mean that when viewing the project as stakeholder-interaction model the from the outside enforced structures prohibits self-organization. The property of self-organization enhances a systems adaptive capacity.

Adaptive capacity

A CAS has adaptive behavior (hence the name), which lets it work better in its environment. The adaptive capacity provides the system the much needed resilience in face of changes in the environment or the system itself.

Attractors

Attractors are not a property of complex systems, however it is a concept that plays a major role in its treatment. Systems will follow certain paths. Attractors help us to define where they are heading for.

“In dynamical systems, an attractor is a set in the phase space to which the system evolves after a long enough time. Phase space is the space in which all possible states of a system are represented, with each possible state of the system corresponding to one unique point in the phase space. A trajectory of the dynamical system in the attractor does not have to satisfy any special constraints except for remaining on the attractor. The trajectory may be periodic or chaotic or of any other type.” (Source: Wikipedia)

The state of a system is something that the one defining the system can determine. If you are discussing projects, we are free to define which states we want to consider. To give you some idea: if we consider communication patterns within the project team, we can have the situation that everybody communicates with everybody else (fully connected communication graph), or we can have the situation that everyone only communicates though the PM (graph looks like a wheel). The phase space can consist of the connectivity of the communication network.