Subjective Perspectives within Learned Representations Predict High-Impact Innovation

2025-06-05

AI Summary

Prior research predominantly examines how social structures influence innovation, neglecting innovators’ subjective perspectives and the quantitative characterization of their interpersonal innovation opportunities. Method: We propose a dynamic language-representation framework to construct a conceptual geometric space, mapping innovators’ historical experience trajectories onto their subjective perspectives and latent combinatorial opportunities—enabling prediction of future creative attention and successful combinations. Contribution/Results: We establish “perspective diversity” (rather than background diversity) as the primary predictor of cross-domain creative achievement. This finding holds across five domains—science, technology, film/TV, entrepreneurship, and Wikipedia—with robust positive effects; conversely, experiential background differences exhibit negative associations. Our approach integrates high-dimensional geometric modeling, large-scale trajectory analysis, natural experiments, and LLM-agent collaborative simulation. Empirical and synthetic validation confirms methodological robustness and generalizability.

Technology Category

• Cognitive Modeling & Cognitive Systems: Computational Creativity
• Application Domains: Humanities & Computational Social Science
• Knowledge Representation and Reasoning: Preferences

Application Category

• Social Networks and Social Media: Generative AI / large language models and their impact on social systems
• User Modeling, Personalization and Recommendation: Psychology-informed user models and recommender systems
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions

Abstract

Existing studies of innovation emphasize the power of social structures to shape innovation capacity. Emerging machine learning approaches, however, enable us to model innovators' personal perspectives and interpersonal innovation opportunities as a function of their prior trajectories of experience. We theorize then quantify subjective perspectives and innovation opportunities based on innovator positions within the geometric space of concepts inscribed by dynamic language representations. Using data on millions of scientists, inventors, writers, entrepreneurs, and Wikipedia contributors across the creative domains of science, technology, film, entrepreneurship, and Wikipedia, here we show that measured subjective perspectives anticipate what ideas individuals and groups creatively attend to and successfully combine in future. When perspective and background diversity are decomposed as the angular difference between collaborators' perspectives on their creation and between their experiences, the former consistently anticipates creative achievement while the latter portends its opposite, across all cases and time periods examined. We analyze a natural experiment and simulate creative collaborations between AI (large language model) agents designed with various perspective and background diversity, which are consistent with our observational findings. We explore mechanisms underlying these findings and identify how successful collaborators leverage common language to weave together diverse experience obtained through trajectories of prior work that converge to provoke one another and innovate. We explore the importance of these findings for team assembly and research policy.

Problem

• Model innovators' personal perspectives and opportunities using machine learning
• Quantify subjective perspectives via geometric space of dynamic language representations
• Analyze how perspective diversity impacts creative achievement in collaborations

Innovation

• Model innovators' perspectives using machine learning
• Quantify subjective perspectives via geometric language representations
• Simulate creative collaborations with diverse AI agents