# Tradition and Innovation in Scientists' Research Strategies

arXiv: https://arxiv.org/abs/1302.6906 · HTML: https://mlsanity.com/paper/1302.6906/

## Key facts

- Cited by 26 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 27 Feb 2013.

## Built on by (most cited first, 26 of 26)

- [Mapping the Increasing Use of LLMs in Scientific Papers](https://mlsanity.com/paper/2404.01268/) (2024)
- [Predicting the Future of AI with AI: High-quality link prediction in an exponentially growing knowledge network](https://mlsanity.com/paper/2210.00881/) (2022)
- [Forecasting high-impact research topics via machine learning on evolving knowledge graphs](https://mlsanity.com/paper/2402.08640/) (2024)
- [Words as Gatekeepers: Measuring Discipline-specific Terms and Meanings in Scholarly Publications](https://mlsanity.com/paper/2212.09676/) (2022)
- [From Protoscience to Epistemic Monoculture: How Benchmarking Set the Stage for the Deep Learning Revolution](https://mlsanity.com/paper/2404.06647/) (2024)
- [A review on the novelty measurements of academic papers](https://mlsanity.com/paper/2501.17456/) (2025)
- [Evolving Roles of LLMs in Scientific Innovation: Assistant, Collaborator, Scientist, and Evaluator](https://mlsanity.com/paper/2507.11810/) (2025)
- [Hierarchical Interdisciplinary Topic Detection Model for Research Proposal Classification](https://mlsanity.com/paper/2209.13519/) (2022)
- [Promotional Language and the Adoption of Innovative Ideas in Science](https://mlsanity.com/paper/2406.02798/) (2024)
- [Agentic AI Scientists Are Not Built For Autonomous Scientific Discovery](https://mlsanity.com/paper/2605.08956/) (2026)
- [Who Should Review Your Proposal? Interdisciplinary Topic Path Detection for Research Proposals](https://mlsanity.com/paper/2203.10922/) (2022)
- [AI Research Agents Narrow Scientific Exploration](https://mlsanity.com/paper/2605.27905/) (2026)
- [Hierarchical MixUp Multi-label Classification with Imbalanced Interdisciplinary Research Proposals](https://mlsanity.com/paper/2209.13912/) (2022)
- [Structuring Scientific Innovation: A Framework for Modeling and Discovering Impactful Knowledge Combinations](https://mlsanity.com/paper/2503.18865/) (2025)
- [Time to Cite: Modeling Citation Networks using the Dynamic Impact Single-Event Embedding Model](https://mlsanity.com/paper/2403.00032/) (2024)
- [Big Data and the Computational Social Science of Entrepreneurship and Innovation](https://mlsanity.com/paper/2505.08706/) (2025)
- [Density, asymmetry and citation dynamics in scientific literature](https://mlsanity.com/paper/2506.23366/) (2025)
- [Emergent evaluation hubs in a decentralizing large language model ecosystem](https://mlsanity.com/paper/2510.01286/) (2025)
- [MARBLE: Multi-Agent Reasoning for Bioinformatics Learning and Evolution](https://mlsanity.com/paper/2601.14349/) (2026)
- [War in the Abstract: The Rise and Consequences of Militarized Language in Scientific Communication](https://mlsanity.com/paper/2606.23462/) (2026)
- [Exploring the relationship between team institutional composition and novelty in academic papers based on fine-grained knowledge entities](https://mlsanity.com/paper/2606.31058/) (2026)
- [A framework for single and multi-agent human-AI curiosity ecosystems](https://mlsanity.com/paper/2607.06214/) (2026)
- [Discovery by Dreaming: Cross-Domain Recombination in Artificial Memory](https://mlsanity.com/paper/2607.16256/) (2026)
- [Has Scientific Talent Shifted from Depth to Breadth?Evidence across Papers, Knowledge Inputs, Careers, and Teams](https://mlsanity.com/paper/2609.14425/) (2026)
- [Doc2LoRA Provides Decodable Representations of Scientific Ideas](https://mlsanity.com/paper/2609.38374/) (2026)
- [Science Utopia? Closed-Loop LLM Simulation of Academic Research Ecosystems](https://mlsanity.com/paper/2610.01257/) (2026)

## Abstract (arXiv)

What factors affect a scientist's choice of research problem? Qualitative research in the history, philosophy, and sociology of science suggests that this choice is shaped by an "essential tension" between the professional demand for productivity and a conflicting drive toward risky innovation. We examine this tension empirically in the context of biomedical chemistry. We use complex networks to represent the evolving state of scientific knowledge, as expressed in publications. We then define research strategies relative to these networks. Scientists can introduce novel chemicals or chemical relationships--or delve deeper into known ones. They can consolidate existing knowledge clusters, or bridge distant ones. Analyzing such choices in aggregate, we find that the distribution of strategies remains remarkably stable, even as chemical knowledge grows dramatically. High-risk strategies, which explore new chemical relationships, are less prevalent in the literature, reflecting a growing focus on established knowledge at the expense of new opportunities. Research following a risky strategy is more likely to be ignored but also more likely to achieve high impact and recognition. While the outcome of a risky strategy has a higher expected reward than the outcome of a conservative strategy, the additional reward is insufficient to compensate for the additional risk. By studying the winners of 137 different prizes in biomedicine and chemistry, we show that the occasional "gamble" for extraordinary impact is the most plausible explanation for observed levels of risk-taking. Our empirical demonstration and unpacking of the "essential tension" suggests policy interventions that may foster more innovative research.
