Complexity, Cybernetics, and Informing Science: Building a Better Mousetrap
T. Grandon Gill
Our decision-making and task environments are driven by three forms of complexity: complexity as we experience it internally (e.g., difficulty, uncertainty, ambiguity), complexity as it relates to our symbolic representation of tasks and plans (e.g., number of paths, program size), and complexity as a description of the decision environment and its behavior (e.g., ruggedness, turbulence). When experiencing high levels of complexity, we respond by constructing informing systems that better connect us together and offer increasingly rapid access to more information sources. In doing so, however, we inadvertently feed a cybernetic loop that leads to ever-expanding complexity (in all three forms). Left unchecked, this loop has the potential to alter both the way we think and the environments we face in ways that we may not desire.
Building a better mousetrap requires us to rethink both our approach to education and to designing systems. On the education side, we need to spend less time emphasizing specific content and more on building the student’s the ability to react to complexity in ways that do not rely on making the world more complicated. On the design side, systems must increasingly emphasize adaptability as opposed to efficiency.