Richard A Blythe

Prof Blythe's research concerns modelling and understanding complex far-from-equilibrium systems. These systems abound at all scales, from cells through driven materials to evolving ecosystems and societies. As yet we lack a complete theory for predicting the emergent macroscopic consequences of the interactions between the constituents of these systems. Until this is achieved, such long-term goals as controlling materials driven by industrial processes, or forecasting the future behaviour of an ecosystem or a society will remain out of reach. As such, emergence and physics far from equilibrium has been recognised as a Physical Science Grand Challenge for the 21st Century. Prof Blythe's research directly addresses this challenge in two ways. First, he investigates far-from-equilibrium systems at a fundamental level by means of mathematical analyses of models to establish how basic properties of a system (e.g., symmetries, conservation laws and spatial heterogeneity) govern their macroscopic behaviour. Second, there are many opportunities to apply quantitative techniques from the physical sciences to test theories in other disciplines in new ways. A major part of his recent research has involved applications of modelling in the context of human linguistic behaviour at various levels of description. The nature of evolving linguistic systems – whereby patterns of behaviour at the level of societies emerge from a combination of human cognition and social interactions – makes them a natural fit to the paradigmatic approach of statistical physics that attempts to explain patterns in macroscopic systems with many constituents from knowledge of the interactions between component parts.

[2025] Exploiting individual differences to bootstrap communication, PLOS Complex Systems, 2(11), p. 1-23. [2025] The cost of resetting discrete-time random walks, Journal of Statistical Mechanics: Theory and Experiment, 2025(8), p. 1-29. [2024] Minimizing the Profligacy of Searches with Reset, Physical Review E, 110(5), p. 1-11. [2024] Learning spatio-temporal patterns with Neural Cellular Automata, PLoS Computational Biology, 20(4), p. 1-27. [2023] Reliable Detection and Quantification of Selective Forces in Language Change, Corpus Linguistics and Linguistic Theory, p. 1-43.