Building computational models in any discipline has many challenges starting at inclusion (what goes in, what’s left out), through to representation (are we keeping track of aggregate numbers, or actual individuals), implementation (efficiency, cost) and finally verification and validation (is the model correct?). Creating entire modeling software platforms intended for end-user scientists within a discipline brings an entirely new level of challenge. Cognitive issues of representation within the modeling platform – always present when trying to communicate the content of a model to others – become one of the most central challenges. To create modeling platforms that, say, a biologist might want to use, requires paying close attention to the idioms and metaphors used at the most granular level of biology: at the whiteboard, the bench, or even in the field.
Constructing such software with appropriate metaphors, visual or otherwise, is a process of close collaboration with working scientists at every step. For example, when I was working as a scientist and applications developer on the agent-based modeling toolkit, Swarm, we were constantly refining our interfaces and idioms based on feedback from the scientists using the tool (which were often ourselves!), so that features common to those working in complex adaptive systems, such as collections of agents, and schedules of events could be represented in code in the most direct way possible. Many of these basic Swarm idioms have since been replicated in many other agent-based modeling tools, including RePast, MASON and others. For this reason, complex systems researcher, Gary An, has sometimes called Swarm the “Latin” of agent-based modeling.
In a similar vein, The Digital Biologist has an excellent blog post on the importance of user experience design when creating modeling platforms in molecular systems biology. Pointing out that models are about more than prediction, they are also a way communicating, sharing and explaining the underlying system to other scientists. For this reason modeling frameworks will only be successful insofar as they speak the vernacular of the field:
In fields like biology where exposure to computational models is more limited, there is a tendency to consider their utility largely in terms of their ability to make predictions – but what often gets overlooked is the fact that models also facilitate the communication and discussion of concepts by serving as cognitive frameworks for understanding them.
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For a model to be truly useful and meaningful in a particular field of intellectual activity, it needs to support the conceptual idioms by which ideas and knowledge are shared by those in the field.
The blog post describes a particularly striking design experiment, where inclusion of a very simple visual representation of a standard molecular biology experiment: the Western blot along with a more standard time-series plot, made a huge difference to how biologists viewed the simulation:
…. the version of the interface with the Western blot display made a great deal more sense to them because it helped them to make the mental leap between the data being output from the model and what the model was actually telling them. In their minds and perhaps most importantly, it also reinforced the idea of the computational simulation as a virtual experiment whose results could help guide their decisions about which physical experiments to do in the lab.
Biosystems Analytics 