A Novel Mathematical Framework for Objective Characterization of Ideas

TL;DR

This paper introduces a mathematical framework that objectively evaluates and selects promising ideas generated by AI systems or humans, improving the efficiency and reliability of the ideation process.

Contribution

The study presents a novel quantitative method using high-dimensional vectors and clustering tools to assess idea diversity and quality, reducing reliance on subjective human judgment.

Findings

Effective in distinguishing promising ideas from diverse sets

Reduces bias and errors in idea evaluation

Enhances ideation efficiency for novice designers

Abstract

The demand for innovation in product design necessitates a prolific ideation phase. Conversational AI (CAI) systems that use Large Language Models (LLMs) such as GPT (Generative Pre-trained Transformer) have been shown to be fruitful in augmenting human creativity, providing numerous novel and diverse ideas. Despite the success in ideation quantity, the qualitative assessment of these ideas remains challenging and traditionally reliant on expert human evaluation. This method suffers from limitations such as human judgment errors, bias, and oversight. Addressing this gap, our study introduces a comprehensive mathematical framework for automated analysis to objectively evaluate the plethora of ideas generated by CAI systems and/or humans. This framework is particularly advantageous for novice designers who lack experience in selecting promising ideas. By converting the ideas into higher dimensional vectors and quantitatively measuring the diversity between them using tools such as UMAP, DBSCAN and PCA, the proposed method provides a reliable and objective way of selecting the most promising ideas, thereby enhancing the efficiency of the ideation phase.