Benedict Singleton on Platform Design

Platforms have their own logic: they work best when they don’t address a specific problem or respond to a predefined ‘need’ or ‘desire’, but instead solicit appropriation in unexpected ways, and find their reason for being through ongoing use. As we will see, this can create scenarios that seem strange when we’re used to thinking in terms of products or services: the platform is like a cross between an object that doesn’t have a defined point, and a plan that relies on the unforeseen in order to work. In this apparent paradox, however, we find a surprisingly consistent, and consistently surprising, way to rethink some of the basic parameters of design.

Narratives

Timothy F. H. Allen, theoretical ecologist and founder of ecological hierarchy theory, has put forward one perspective on methods for conceptualizing socio­ ecological issues: the narrative. A narrative is a theoretical context within which more reductionist views can be interpreted. According to Allen, a narrative is a set of elaborate scaling operations that make things of different sizes commensu rate – earthquake, pestilence, and drought. You can make them commensurate by turning them into events. Thus, generally speaking, the point of science is to improve the quality of the narratives it tells. It uses models to do this, but it also uses narratives. Ultimately, science tells stories. In this perspective, Allen appears to take us a step forward in showing an intrinsic interrelationship between quantitative and qualitative methods. It’s not just that we need them both, but also, that they are in many ways already intertwined, and we benefit from making this more explicit.

Models

Models, all models, are simple relative to full-blown complex systems. They are really just analogies and metaphors for the behavior of the system. It is very important to do simple models, oscillator models of climatic behavior. Then, though, you add complexity, you build up the hierarchy, and you find out how each addition of incremental change alters the emergent properties of your answer, the results of your experiments. Through that process you are building up your understanding. You are rendering your models more sophisticated, more real-life-like.