Using Soft Systems Methodology to Explore Networks

Welcome to my blog on systems thinking in collaborative networks. I am interested in a wide range of systems thinking as it relates to network practice, and my current research focuses specifically on the role of fairness in collaboration. This research is being conducted to fulfill the requirements of Masters in Science from the Open University's Systems Thinking in Practice program. The OU is based in the UK, but I am in the US.

In this post, I share my choice of methodology, Soft Systems Methodology, and why I believe it is suited to collaborative networks. In my next post, I provide an overview of Fairness Heuristic Theory and its potential application to networks.

Soft Systems Methodology (SSM)

Soft Systems Methodology (SSM) was developed in the 1980s by Peter Checkland. It has evolved  into a modern form with four stages: 1) finding out about a problematic situation, 2) formulating purposeful activity models, 3) using the models for debate and reaching accommodation among stakeholders, and 4) taking action to improve the situation.  However,  these steps do not necessarily happen in the order listed, or only one time. An SSM intervention is iterative.

Source: Giles A. Hindle in Case Article—Teaching Soft Systems Methodology and a Blueprint for a Module

Why "soft" systems? A key insight Checkland contributed with this methodology was that truly complex situations are not "real" or "hard" in the sense that they can be objectively defined or engineered. A complex system is not simply an extremely complicated technical problem, but is confounding in that it's defined differently, depending on your point of view. The boundary of the system, the output of the system, and the dynamics are dependent on perspective. The role of perspective is bound inextricably with complexity. 

For example, if you point to a truck and ask different people with different points of view the main thing that truck produces,  you might receive any of the following answers: transportation, ability to haul, pollution, prestige, amelioration of a mid-life crisis, or a set of bills. Imagine the truck is jointly owned by people who hold all these perspectives. If the truck stops working, different people will have very different reactions to this, and understanding the complicated mechanics of the truck will only get you so far in being able to improve the problem.

I like Hindle's diagram (above) because it puts the role of multiple perspectives at the center of the SSM intervention. A key part of finding out about the problem is enabling diverse stakeholders to define how they see the situation and the context it sits within. One technique for this is drawing pictures that represent what is happening in the system: the structures, the processes, the relationships. Participants also consider the roles, norms, and values at work in the situation.

Once one has made a good start on finding out about the situation, the group engages in thought experiments to make models of activities that are relevant to the situation, and they use these as a basis for coming to an agreement on how to improve things. These discussions are based not only in what would be desirable, but also what is feasible, considering the relationships, roles, norms, and values already present in the situation.

The final stage of the methodology is taking action, which results in a new and (hopefully) improved, but (likely) still problematic situation, which needs to be explored. This may sound like embracing Sisypheanism, but it's quite different. It's simply recognizing that in complex situations, today's problems are often the result of yesterday's solutions. Hopefully today's problems aren't as bad as yesterday's problems were, because hopefully we have intervened wisely. But if a single set of actions could solve the problem, it probably wasn't so complex to begin with. In SSM, we have a tool for managing complexity, rather than for being a superhero who solves everyone's problem.

SSM and Collaborative Networks

I draw from many different fields of Systems Thinking in my work with Networks, but I find  SSM particularly well-suited to networks because SSM embraces the role of multiple perspectives throughout the entire process. 

SSM is also focused as much on learning as it is on outcomes. Collaborative networks are still emerging as a way of impacting complex situations, and we have so much to learn! By using a methodology that is grounded in learning, we reap both the benefits of improvements we undertake, and we also learn how to learn--collaboratively. 

In my next post, I will talk about Fairness Heuristic Theory and why I am using SSM to explore its application to collaborative networks.