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Updated: Jan 29, 2026
14:14
The Innovation Arena: A Method for Comparing Innovative Problem-Solving Across Groups
Published on: May 13, 2022
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Scaling up analogical innovation with crowds and AI
Aniket Kittur1, Lixiu Yu2, Tom Hope3
1Human-Computer Interaction Institute, Carnegie Mellon University, Pittsburgh, PA 15213; nkittur@cs.cmu.edu.
Summary
Distributed analogical reasoning, using crowds and artificial intelligence (AI), can accelerate scientific innovation by overcoming cognitive limits and scaling idea exploration. This approach promises to enhance creativity for complex problem-solving.
Area of Science:
- Cognitive Science
- Artificial Intelligence
- Innovation Studies
Background:
- Analogy, the identification and application of structural patterns across domains, is crucial for scientific and technological innovation.
- Current innovation is often limited by individual cognitive constraints and the difficulty of searching vast idea repositories.
Purpose of the Study:
- To propose a framework for scalable analogical innovation by distributing reasoning across human and machine processors.
- To explore the potential of crowdsourcing and artificial intelligence (AI) to augment human creativity and problem-solving capabilities.
Main Methods:
- Conceptual framework development for distributed analogical reasoning.
- Integration of crowdsourcing and AI techniques to support analogical discovery.
- Quantitative demonstration of the approach's promise and identification of key challenges.
Main Results:
- Distributed analogical reasoning can overcome individual cognitive fixation.
- The approach scales effectively to large repositories of ideas.
- AI and crowds can augment human creativity for complex, multi-constraint problems.
Conclusions:
- Distributing analogical reasoning across humans and AI offers a powerful new paradigm for accelerating innovation.
- Further research is needed to address core challenges in realizing the full potential of scalable analogical innovation.
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