Persistence Paradox in Dynamic Science

2025-06-27

AI Summary

This study challenges the assumption that persistence is invariably virtuous in science, examining the adaptation paradox during paradigm shifts. Method: Leveraging the 2012 AlexNet-driven deep learning revolution as a natural experiment, we track the 20-year career trajectories of over 5,000 computer scientists, integrating bibliometric analysis, citation percentile ranking, and career-path modeling. Contribution/Results: We introduce the concept of “rigidity penalty,” revealing that top scholars in traditional machine learning experienced significant declines in influence due to path dependence. In contrast, researchers maintaining weak interdisciplinary ties and strategically pivoting to the new paradigm exhibited greater increases in research output and impact. The findings demonstrate that paradigm shifts not only reconfigure knowledge structures but also redistribute epistemic authority—providing micro-level empirical evidence on individual adaptive mechanisms and institutional resilience in scientific evolution.

Technology Category

• Game Theory and Economic Paradigms: Adversarial Learning
• Cognitive Modeling & Cognitive Systems: Adaptive Behavior
• Knowledge Representation and Reasoning: Action, Change, and Causality

Application Category

• Web Mining and Content Analysis: Models for Web evolution
• Social Networks and Social Media: Computational social science
• Economics, Online Markets and Human Computation: The sharing economy

Abstract

Persistence is often regarded as a virtue in science. In this paper, however, we challenge this conventional view by highlighting its contextual nature, particularly how persistence can become a liability during periods of paradigm shift. We focus on the deep learning revolution catalyzed by AlexNet in 2012. Analyzing the 20-year career trajectories of over 5,000 scientists who were active in top machine learning venues during the preceding decade, we examine how their research focus and output evolved. We first uncover a dynamic period in which leading venues increasingly prioritized cutting-edge deep learning developments that displaced relatively traditional statistical learning methods. Scientists responded to these changes in markedly different ways. Those who were previously successful or affiliated with old teams adapted more slowly, experiencing what we term a rigidity penalty - a reluctance to embrace new directions leading to a decline in scientific impact, as measured by citation percentile rank. In contrast, scientists who pursued strategic adaptation - selectively pivoting toward emerging trends while preserving weak connections to prior expertise - reaped the greatest benefits. Taken together, our macro- and micro-level findings show that scientific breakthroughs act as mechanisms that reconfigure power structures within a field.

Problem

• Challenges persistence virtue during paradigm shifts
• Examines rigidity penalty in adapting to new trends
• Analyzes career impacts of strategic adaptation

Innovation

• Analyzed 5000 scientists' career trajectories
• Identified rigidity penalty in adaptation
• Proposed strategic adaptation to new trends