A framework for assessing domain transfer risk in biologically inspired design

Inherent in Biomimetic design is the selection of one or more biological analogues from which one or more strategies are extracted and transferred into the engineering domain. This cross-domain transfer (Biology to Engineering) is what distinguishes Biomimetic Design from “normal” engineering design. However, before cross-domain transfer can occur, the designer or design team must choose a biological analogue. It is the methodology for how analogue choice is made that is missing from the current literature. The purpose of this research is to develop a framework to assist in biological analogue selection. The framework will enable decision makers (DMs) to evaluate biology-to-engineering transfer risk for multiple candidate design analogs. Thus, the framework will guide DMs to a low-risk choice by using externally valid predictors of domain transfer risk. The development of the framework draws primarily from three fields of study: Biology, Engineering, and Cognitive Science. Biomimetics is the integration of Biology and Engineering – and thus a large portion of the research was based upon understanding the current practice of Biomimetic Design. However, in order to develop a decision framework, it was necessary to first understand how decisions are made. Cognitive science in general and Decision theory specifically provide a large body of research and methods to be considered for the framework. This research focus is on Multi-Criteria Decision Methodologies (MCDM) as they offer a robust number of methodologies and active research from which to draw for group decision-making. The research objective is met with the development of the Bio-Transferability Risk Assessment Framework. The framework provides a means for both evaluating biology-to-engineering transfer risk and facilitating BID communication in transdisciplinary teams. The framework process is iterative with decision model feedback serving to focus discussion on specific criteria, candidates, or both. Analysis of the decision model results provides an opportunity for refinement while promoting transdisciplinary communication among DMs.