A FORMULATION OF THE PRELIMINARY DESING PHASE USING COMPLEXITY-BASED RULES
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
This paper focuses on the application of complexity theory and entropy concepts in the design process. Broadly speaking, the design process involves three phases: problem definition, conceptual design and embodiment. In the conceptual design phase, concepts that satisfy the functional requirements of the desired product are identified and compared. It is said that approximately 75% of the total product life-cycle cost is committed in this phase. The conceptual design phase has two essential sub-phases, namely, obtaining a solution set and selecting the most suitable solutions. Our work focuses on the selection sub-phase. The aim within this sub-phase is to minimize the number of selected concept variants and to reduce their chances of rejection in later stages. However, the solution to this problem is quite elusive, mostly because information about concept variants is scarce and rather qualitative at this stage. A common method is to perform a cost-benefit analysis. However, the analysis relies heavily on expert intuition and is thus subjected to high uncertainties. Recently, axiomatic design is gaining popularity. This is a framework that incorporates two axioms, namely, the Independence Axiom and the Minimum Information Axiom, accompanied by several corollaries. However, criticism on the integrity of the Independence Axiom has appeared recently in the literature. Further, the formulation of axiomatic design appears to have logical flaws. Finally, the conceptual design phase, a distinct phase in the design process, cannot be distinguished clearly in axiomatic design. In this paper we try to improve the selection phase of the conceptual design by improving the existing cost-benefit approach. In this vein, performance features against which concepts would be evaluated are established. We propose the use of Kolmogorov complexity theory and entropy concepts from information theory and physics to evaluate the complexity of the performance features. The design concepts are then improved based on the rule to reduce complexity of each performance feature. Weights are finally assigned to each performance feature and an overall complexity index is obtained which is suitable to compare designs. The ideas are further elaborated on by examples.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it