Fitness, Extrinsic Complexity and Informing Science

Abstract

Aim/PurposeWe establish a conceptually rigorous definition for the widely used but loosely defined term "fitness". We then tie this definition to complexity, highlighting a number of important implications for the informing science transdiscipline. BackgroundAs informing science increasingly incorporates concepts of fitness and complexity in its research stream, rigorous discussion and definition of both terms is essential to effective communication. MethodologyOur analysis consists principally of a synthesis of past work in the informing science field that incorporates concepts from evolutionary biology, economics and management. ContributionWe provide a rigorous approach to defining fitness and introduce the construct "extrinsic complexity", as a measure of the amount of information required to predict fitness, to more fully differentiate this form of complexity from other complexity constructs. We draw a number of conclusions regarding how behaviors under low and high extrinsic complexity will differ. Findings High extrinsic complexity environments are likely to produce behaviors that include resistance to change, imitation, turbulence and inequality. Recommendations for PractitionersAs extrinsic complexity grows, effective search for problem solutions will increasingly dominate employing recommended solutions of "best practices". Recommendation for ResearchersAs extrinsic complexity grows, research tools that rely on decomposing individual effects and hypothesis testing become increasingly unreliable. Impact on Society We raise concerns about society's continuing investment in academic research that discounts the extrinsic complexity of the domains under study. Future ResearchWe highlight a need for research to operationalize the concepts of fitness and complexity in practice.

Gifts, Contexts, Means, and Ends Differing: Informing Task Scenarios to Serve Knowledge Workers’ Needs in Dynamic Complex Settings

“…The ancestry of this article includes a four-paper-lineage which commenced with an exploratory case research study to understand the informing-related complexities of events affecting diverse parties by interpreting them as rugged fitness landscapes (Murphy et al, 2015). This approach expanded towards generalizable fitness principles for guiding individual path discovery for differing clients in dynamic informing settings (Gill & Mullarkey, 2017). It was then applied to Personal KM (Schmitt & Gill, 2019) and broadened by adding contextual and methodological aspects (Schmitt & Gill, 2020).…”

Synthesizing Design and Informing Science Rationales for Driving a Decentralized Generative Knowledge Management Agenda

“…From an informing perspective, a particularly challenging situation arises when an informing system must meet the needs of multiple clients, many of whom may have quite different objectives in being informed. One approach to visualizing this challenge, particularly likely in dynamic environments, is to picture informing activities as transitions between peaks in a rugged fitness landscape (Gill & Mullarkey, 2017;Murphy, Murphy, Buettner, & Gill, 2015). Combining the states of (single and multiple) starting and possible ending peaks among (single or diverse) clients allows for the clustering of four distinct quadrants ( Figure 2) with distinct intervention scenarios.…”

Informing on a Rugged Landscape: How Complexity Drives Our Preferred Information Sources

“…Typically, earlier research assumes that (1) fitness is an ordinal value whose relationship to the biological notion of survival is limited at best, (2) the relationship between attributes and fitness is relatively stable over time, and (3) agents can immediately assess the fitness of a state once they occupy it. Using state-sets, a much tighter linkage between fitness and survival/reproduction can be established (Gill & Mullarkey, 2017). We refer to this revised definition as set-fitness while discussing fitness assumed by prior rugged fitness landscape models as state-fitness.…”

“…Many plausible proxies have a social dimension, which is to say that they depend upon the behaviors and performance of other stakeholders, competitors, or observers. Examples include expert rankings as in Consumer Reports product ratings or published university rankings, popularity counts such as YouTube views, consensus ratings such as Netflix star ratings, observed behaviors of competitor or other organizations, and benchmarks (Gill & Mullarkey, 2017).…”

“…We believe, however, that the nature of the environment faced by the client proves to be an important factor in determining the most appropriate sources of information. In this paper, we look at one environmental characteristic-extrinsic complexity (Gill & Mullarkey, 2017)-and develop a theoretical model of how this characteristic might guide clients towards general categories of informers.…”