How clear is the network from the perspective of a participant or observer? Are the connections easily visible or are they hidden? The opacity of a network can influence how much agency each actor has and help to create the desired texture.

In our traffic-light example, we see a very opaque system, one where the means of interacting are often completely hidden. It would be easy to interact with the system and still not even know that it exists. In this example, the opacity has a direct impact on a person’s agency, but if the system behaves properly, the texture might still be smooth. Roughness will become apparent if the system misbehaves and nobody can see what is happening.


How do you know what is happening in the network? How does it inform the different actors, both human and nonhuman, what state it is in and if there are any problems? Feedback and communication is a vital piece of any system.

Reflexivity is the way in which the particular system provides feedback based on states, actions, and behaviors. This is an indication that the rules of the system are enforced. By providing feedback when a component attempts an action the system can let all of its parts know what is happening, if the action was completed, and what the new state looks like. The quality of this feedback is important to crafting the aesthetic of the system. Is it friendly? Verbose? Human readable? All of these things will change the overall feel of the products and services that are part of the network.

These are some possible aesthetic elements we can begin to use to discuss the qualities of a network system. None are inherently good or bad; they are the basis for a common language that lets us discuss the aspects of a network that affect its quality. An opaque network with little agency creates a certain type of interaction, one largely dictated by its owner. A low-opacity network with a lot of agency allows for more flexibility and potential wrangling by the person interfacing with the system.

The types of systems and products described by the above aesthetic language can be understood in two important ways (among others):

  • 1. As a hard system: a system model that is concrete and constructed to achieve an objective. These types of systems are easy to analyze and model because they are generally made up of discrete pieces that each plays a set part, most often actual things that exist in the physical world.
  • 2. As a soft system: a system model that is fuzzy and focuses on the understanding of the system from many perspectives. In this type of model each piece of the system is based on a subjective understanding of the whole, rather than specific objects that exist in the world.

For the type of design discussed in this chapter we are more concerned with soft systems, although both soft and hard must exist in order to fully understand and build a product or service in our networked world.

Soft systems methodology (SSM), a framework for thinking about epistemological systems, gives us tools to help understand an unstructured complex problem through modeling actions and subjective understanding of the situation. Unlike hard systems, soft systems models aren’t about classification; instead the practice seeks to explain different relationships by describing them as they are seen, understood, and acted upon. A single set of objects and relationships could be described in many different ways, each one equally valid from a different perspective. Soft systems have always had a close tie to the way designers work. Peter Checkland, one of the SSM pioneers, said the following in his book Systems Thinking, Systems Practice:

Its rationale lies in the fact that the complexity of human affairs is always a complexity of multiple interacting relationships; and pictures are a better medium than linear prose for expressing relationships. Pictures can be taken in as a whole and help to encourage holistic rather than reductionist thinking about a situation

Design’s tradition of visualization and sketching fit very well with SSM’s tendency toward visualization from the perspective of an actor within the system. In the networked world the designer’s ability to understand, explore, and explain complex interactions between people and machines, and machines to machines, becomes even more important. SSM gives us a starting point to understand how to reframe complex situations through a process that begins by embedding oneself into the situation, expressing what you observe and understand that situation to be, and then creating diagrams that express that understanding. Once the system is visualized it can be compared to observed reality to understand which definition fits best in the given context and what actions one should take to affect the system, described in SSM as feasible and desirable changes. The use of visual tools helps the designers and stakeholders build the same mental model, rather than the ambiguity of individual conceptions.

Tools like this one become a primary piece of the twenty-first century designer’s kit. Making sense of and expressing complex systems of relationships, communication, and feedback lay the foundation for good design decisions when dealing with complex networks, invisible interfaces, and nuanced interactions.

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