A Framework for Early Design Process Stages Based on an Analogy to Evolution
Bibliographic record
Abstract
Recent research has revealed several shortcomings of design processes with respect to modern contexts. Two of these are complexity and sustainability. Design problems are becoming increasingly complex, to the point where designers can be easily overwhelmed. Sustainability, while recognized as pivotal to future human progress and well-being, remains largely disconnected from design processes. Current practices in the field of sustainability are not integrated into the design process and thus, are often carried out only as after-the-fact addenda. The goal of this research is to address these two problems with current design processes. It has long been known that analogies are useful and help to reduce complexity by rooting a topic into pre-existing knowledge of the user. Patterns have also been shown to be useful in helping solve complex problems in engineering, as well as architecture and computer science. Therefore, this dissertation proposes a new design framework which: reduces problem complexity through the use of an analogy and patterns, makes provisions for emergent properties within the patterns and the framework, and provides a means for generating solutions with aspects of sustainability via the patterns and evaluation criteria. The analogical framework, based on similarities found in the phenomena and processes between natural systems (nature) and design, is then used to formulate a new model for describing the design of a product, the Design Genome. The main focus of this dissertation is to use this model as a basis for a new method of concept generation, the Design by DNA method, and concept evaluation, the Fitness Space method. It is shown that the Fitness Space method has the potential to solve many, if not all, of the downfalls of conventional evaluation methods. A pilot experiment testing the Design by DNA method against previously known design methods is conducted and demonstrates the feasibility for a full-scale experiment. Even with small population sizes, results from the experiment show promise that the DbD method is useful as a tool for the concept generation process. Based on the work done, it appears that Design by DNA and the Fitness Space are promising approaches for improving design processes.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.014 | 0.015 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.003 | 0.022 |
| Scholarly communication | 0.008 | 0.013 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".