About the PI: Sergi Valverde
(also known as Sergio Valverde)
Dr. Sergi Valverde is a complex systems scientist and head of the Evolution of Networks group at the Institute of Evolutionary Biology (CSIC). Born in Barcelona, he studied computer science and physics at the School of Informatics (FIB) of the Universitat Politècnica de Catalunya (UPC) and worked as a professional game developer from 1997 to 2002. Sergi's scientific career was shaped by ICREA Professor Ricard Solé (Complex Systems Lab, 2003-2016) and enriched through numerous visits to the Santa Fe Institute. From 2009 to 2020, he taught "Complexity Science" and "Evolutionary Algorithms" as an assistant professor in the Department of Biomedical Engineering, Universitat Pompeu Fabra.
Sergi collaborates actively with the Node Lab at the Centre for Mathematical Research (Dr. Josep Sardanyes) and the University of Tennessee at Knoxville (Prof. R. Alex Bentley). Additionally, he works with esteemed researchers such as Prof. Andrej Spiridonov at Vilnius University, a specialist in theoretical paleontology and Earth systems; Dr. Niles Eldredge, curator emeritus at the American Museum of Natural History and co-creator of the Punctuated Equilibria theory with Stephen J. Gould, which posits that evolution proceeds in rapid bursts separated by periods of stasis (see below); and Prof. Michael J. O’Brien, a leading figure in evolutionary archaeology and biology with extensive publications across major journals like Science and Nature Communications.
Sergi is also a board member of Complexitat.cat, the Catalan Network for the Study of Complex Systems. His research interests include complex networks, collective intelligence, and computational models of evolutionary and ecological processes. Currently, he is working on an evolutionary theory of technological innovation.
- @svalver
- Institut de Biologia Evolutiva (CSIC-UPF)
- Department of Experimental and Health Sciences (UPF)
Research Interests
Punctuated Evolution
Our research explores the dynamics of punctuated evolution, expanding the theory beyond paleontology and evolutionary biology. In recent work, such as "On the multiscale dynamics of punctuated evolution" (Trends in Ecology and Evolution, 2024), we integrate computational models and observations from molecular biology, anthropology, and sociotechnology (see box). This approach provides a framework applicable to both ancient extinctions and potential future disruptions. Additionally, "The many ways toward punctuated evolution" (Palaeontology, 2024) emphasizes the importance of distinguishing between punctuated equilibria in the fossil record and the broader concept of punctuated evolution, which includes eco-evolutionary feedbacks and external disruptions across various systems. In "Punctuated equilibrium at 50: Anything there for evolutionary anthropology? Yes; definitely" (Evolutionary Anthropology, 2023), we revisit foundational aspects of the theory to propose a model relevant to biocultural evolution, helping interpret shifts in cultural and biological diversity under increasing ecological pressures.
Together, these studies underscore punctuated evolution as a unifying concept with broad applications, enhancing our understanding of life’s history and offering a framework for anticipating future evolutionary challenges.
The Punctuated Evolution of Culture and Technology through Phylogenetic Analysis
Darwin famously argued that human language evolved through variation, selection, and inheritance, akin to organismal evolution. In "The Descent of Man," he noted that ‘the formation of different languages and of distinct species, and the proofs that both have been developed through a gradual process, are curiously parallel.’ This concept has influenced ideas of language evolution, drawing attention to the similarities and differences in adaptations over time. Phylogenetic approaches to cultural evolution extend beyond languages and human culture, reconstructing homology from morphological similarities—a perspective long rooted in biology. Despite imperfect mapping between genetic and cultural evolution, parallel principles can be identified: (i) the presence of variation; (ii) transmissibility of variants; and (iii) sorting of variants in successive generations, often through selection, drift, or psychological factors such as mate identification.
The figure above shows (A) the evolution of cornet designs from 1825 onwards, adapted from Tëmkin and Eldredge. Vertical lines represent ‘cultural species,’ covering each design’s lifespan, while horizontal lines denote vertical transfer between them. The phylogeny, based on morphological traits, highlights two major epochs—the shaded Stölzel and unshaded Périnet valve systems—marked by key innovations such as valve count, bell exit position, and shape modifications. Panel (B) depicts a tree of programming languages originating from Fortran (IBM, 1950s), showing macroevolution in software. The vertical axis represents release time, with radiation-like events and languages like Algol 60 and C giving rise to many descendants due to innovations such as the microprocessor. Abbreviation: M, distinct morphologies.
Evolution of Technology
Our research investigates the origins of innovation in cultural and technological evolution, focusing on the role of information networks and their interaction with ecosystems. As a critical yet often invisible technology, software requires quantitative theories of evolution and development. We have pioneered models that treat software systems as complex networks, exploring how principles from information theory can enrich biology and evolutionary studies.
Technological evolution distinguishes human culture, enabling unique capabilities beyond those of other species. However, a comprehensive quantitative theory of technological change is still emerging. Key questions drive our research: How do optimality principles, constraints, and historical contingencies shape network organization? Are selective pressures akin to those in natural evolution applicable to technology? To what degree do recombination and adaptation fuel innovation? Our recent projects, including the Spanish National Grant FIS2016-77447-R, explore these questions.
Check out my YouTube talk "The Ascent of the Computer" at the European Center for Living Technology (Venice, Italy), where I am a research fellow. In this talk, I reviewed the major transitions in information technologies (see also our chapter The Natural Evolution of Computing), with a focus on their connections to biological evolutionary frameworks. We examine the symbiotic evolution of software and hardware, which shows strong social dependencies and extrinsic fitness constraints due to energy and time limitations. Properly accounting for these material and social factors helps explain the coexistence of gradualism and punctuated dynamics in technological evolution.
Dynamics and Evolution of Ecological Networks
In response to accelerating ecological change, our work investigates how environmental heterogeneity influences species adaptation. Beyond empirical data, we aim to build mathematical theories to predict ecosystem responses to disturbances. Our focus includes spatial dynamics and distributed growth within ecological networks, with particular attention to bipartite networks like host-phage interactions.
Traditional network models can overlook critical environmental factors and anthropogenic effects on ecosystem features such as modularity and nestedness. Habitat-mediated interactions suggest a need to extend networks to hypergraphs. In a recent study (Valverde et al., 2020), we applied hypergraph modeling to real-world plant-virus interactions, uncovering the adaptive flexibility of ecosystems facing environmental change (see the short talk below).
Evolution of Artificial Life
Artificial life aims to understand life by creating systems with life-like properties. We explore whether artificial systems can demonstrate creativity and innovation through platforms like Avida and our own Chimera model. Our ongoing research plans include scaling these computational models to simulate large-scale ecosystem evolution.