Reflections on Researching the Rugged Fitness Landscape

Introduction At the core of the informing sciences is the need to better understand the interplay between the components of an informing system: the sender, the client, the delivery system, and the task to be performed. Implicit in this understanding is a notion of fit. For example, if the client is in a rural area of a developing country served only by telephone lines that are subject to intermittent failure and the sender wishes to convey large amounts of information in video format, then the use of an Internet-based delivery system is unlikely to be a good fit with the informing need. Similarly, if the task to be performed is highly interactive and requires ongoing exchange of information with the client, then a system providing two-way communication is likely to be a better fit than a system that only allows one-way broadcast of information. If we attach a numerical or ordinal value to the level of fit, we can refer to that value as a fitness value. If we were to consider a whole variety of different possible informing systems for achieving the same purpose and were to attach a fitness value to each one, we have the beginnings of a fitness landscape. To develop a complete fitness landscape, we would need to develop a function that can take any combination of informing system characteristics and map them to an associated fitness value. The optimal possible system would then be that collection of characteristics that maps to the highest fitness value. It is probably not an exaggeration to assert that most research in the informing sciences is motivated by the desire to contribute, directly or indirectly, to our understanding of the fitness landscape associated with informing. In our goal to better understand the fitness landscape for informing systems, it is useful to learn from other disciplines--many of which have their own versions of the fitness function. In economics, for example, consumers strive to maximize utility--a function that maps the bundle of goods and services they consume to a satisfaction-related value. Producers are frequently modeled as attempting to maximize shareholder value, another measure of fitness. In computer science, the concept of fitness is routinely employed in evaluator functions. A chess program, for example, will normally choose its move based upon assessing the fitness of the alternative board positions that may result. Genetic algorithms use reproductive and mutation rules originally observed in natural systems in an effort to seek solutions of maximal fitness. The concept of fitness is particularly prevalent in biological sciences. Evolutionary biologists, for example, view fitness as a survivability function representing the likelihood of reproductive success that may be applied at many different levels--from the gene to an entire species. If fitness is insufficient, the gene or species ultimately disappears if it fails to evolve or adapt. The present paper is intended to as a non-mathematical introduction to the nature of fitness landscapes, with particular attention being paid to the potential implications for research in the informing sciences. It begins by presenting a continuum used to characterize fitness landscapes that was first introduced in evolutionary biology (e.g., Kauffman, 1993). In this model, landscapes range from decomposable to rugged to chaotic. The paper then demonstrates how the continuum strongly resembles another continuum: that of science and art. The remainder of the paper focuses specifically on rugged landscapes, emphasizing two main themes. First, it argues that the conditions that are likely to lead to a rugged fitness landscape are nearly always going to be present in informing systems. Second, it considers the many ways in which rigorous research conducted on a rugged fitness landscape can--or, more precisely, should--differ from research that assumes underlying decomposability. …