This post summarizes work by Professor Gavin Brown, a lecturer at the University of Manchester, who delivered a captivating talk on a dichotomy of scientific styles: Unifying and Diversifying.

Introduction

Calling upon a plethora of quotations and concepts from a range of philosophical figures, Prof. Brown contends that most sciences, and indeed scientists, are dominated by one of these styles or the other. He described how a Unifying researcher focuses on general principles, seeking out commonalities between concepts to construct all-encompassing explanations for phenomena, while a ‘Diversifier’ ventures into the nitty gritty, exploring the details of a task in search of novel solutions for specific problems. Indeed, as Prof. Brown explained, this fascinating dichotomy maintains science in a “dynamic equilibrium”; unifiers construct rounded explanations that are subsequently explored and challenged by diversifying thinkers. In turn, the resulting outcome fuels unifiers’ instinct to adapt initial explanations to account for the new evidence – and round and round we go.

Examples from the field

Prof. Brown proceeded to demonstrate these processes with example class members from the field. He identifies DeepMind founder, Demis Hassabis, as a textbook ‘Unifier’, utilizing a substantial knowledge of the broad research landscape to connect and combine ideas from different disciplines. Contrarily, Yann LeCun, master of the Convolutional Neural Network, falls comfortably into the ‘Diversifier’ category; he has a focused view of the landscape, specializing on a single concept to identify practical, previously unexplored, solutions.

Relevant Research Strategies

We were then encouraged to reflect upon our own research instincts and understand the degree to which we adopt each style. With this in mind, Prof. Brown introduced valuable strategies that permit the identification of novel and worthwhile research avenues. Unifiers can look under the hood of existing solutions, before building bridges across disciplines to identify alternative concepts that can be reconstructed and reapplied for the given problem domain. Diversifiers on the other hand should adopt a data centric point of view, challenging existing assumptions and, in doing so, altering their mindset to approach tasks from unconventional angles.

Closing Remarks

This fascinating exploration into the world of Unifiers and Diversifiers offered much food for thought, providing listerners with practical insights that can be applied to their broad research methodologies, as well as their day-to-day studies.