Rethinking Precedent Studies: Archetype-Based Dynamic Sense Images and a New Framework for a Formative Exploration

Presented by: Joori Suh

When searching for design ideas before a concrete communicative idea is formed, designers and students go through a “pre-logical” (Root-Bernstein, 2002) stage of abstract thinking where aesthetic sensibility, intuition, and visual impression play an important role. In design education, such abstract thinking starts in various ways, including brainstorming and the early stage of ideation that comes after a precedent study. The problem of pictorial images gathered through precedent studies of other design examples is the static, frozen, and invariable attributes that reside in the photos, which could lead students to maintain a stereotypical imprint in their cognitive process. How can educators encourage designers and students to remove themselves from the static images of precedent studies and transform those frozen images into a dynamic “sense image” (Root-Bernstein, 2002) that could be more useful in the creative ideation process? This study proposes a framework for a formative exploration that emphasizes the transformative quality embedded in archetypes. The study proposes using archetypes as dynamic “sense images” that work as generative abstracts, which do not appear as a single static image but a transformative one with supposedly unlimited variables. Based on Kubler’s (1962) morphological theory of signals and mutants, this study demonstrates the use of the Interactive Genetic Algorithm (IGA) in fabricating a computer-based virtual ideation space that fosters a new dimension in the creative cognitive ideation process using archetypes. The proposed framework of formative exploration includes the following process: First, a number of precedent examples of contemporary design are grouped based on the shared common traits (Jennings, 2009). Second, each group can be defined as a new formative category of archetypes; each archetype is organized based on core principles that define the main characteristics of each archetype and variables that cause multiple transformations of archetypes. Third, these core principles and peripherals are mapped into the proposed computer-based virtual ideation system to create a pool of multiple archetypes as sources of sense images as generative abstractions. Fourth, the proposed system is programmed to visualize the transformative quality of each archetype. While keeping the main set of instructions that define each design archetype, diverse schematic images of each archetype can be produced through mechanisms inspired by biological evolution, such as selection, mutation, crossover, and coevolution. The primary focus of this research is to encourage designers and students to see the transformative quality embedded in archetypes developed from precedent studies and foster creative formative exploration in the pre-logical stage of the ideation process. The implication underlying employing the genetic algorithm in this research is that it allows the malleable structure of archetypes derived from precedent studies to be visible and so that they become dynamic sources of ideation. The result of this research will reframe the way designers use precedent studies by allowing designers and students to observe the hidden opportunities related to each archetype. In addition, this research will redefine formative exploration and possibly facilitate the innovative ideation process.

References:

  • Jennings, J. (2007). A case for a typology of design: The interior archetype project. Journal of Interior Design, 32(3), 48–68.
  • Jennings, J. (2009). “Naming Design Practices: Producing a Body of Knowledge of the Creative Dimension of Interior Design.” Communicating (by) Design. Brussels, Belgium: Sint Lucas School of Architecture in Brussels, Belgium and Department of Architecture at the Chalmers University of Tech
  • Kubler, G. (1962) The shape of time: Remarks on the history of things. Vol. 140. New Haven: Yale UP.
  • Root-Bernstein, R. S. (2002). Aesthetic cognition. International Studies in the Philosophy of Science, 16(1), 61-77.
  • Wu, Z. Y., and Pradeep K. (2009) Applying genetic algorithm to geometry design optimization. Technical Report. Watertown: Bentley Systems.