Reflections on Researching the Rugged Fitness Landscape

The success of an informing system depends upon achieving a fit between multiple entities: the sender, the client, the delivery system, and the task to be performed. Conceptually, the effectiveness of such informing can be modeled as a fitness landscape—a function that maps the characteristics of the system into a scalar fitness value. The assumed shape of that landscape plays a major role in determining how we go about researching a given domain. In the social sciences, we often make the implicit assumption that the processes that we are investigating are largely decomposable, meaning that the impact of a given characteristic on fitness is independent of the values of other characteristics. Many of the statistical tools employed in analyzing social science data, such as regression and structural equation modeling, implicitly depend on such decomposability. In other disciplines, such as evolutionary biology and medicine, the assumption of decomposability is much less prevalent. Instead, the prevalence of interactions between characteristics affecting fitness is taken to be a fact of life, leading to what is called a rugged fitness landscape. This paper explores the nature of such landscapes, the likelihood that they will be encountered in the context of informing systems research, and the implications of ruggedness on how we approach research design.