Embracing Complexity: Abstracts
Below, you will find the abstracts submitted for the workshop "Embracing Complexity: Principled and Practical Approaches to Emergence".
Beyond Reduction and Emergence: A Distributed View of Minds
Antonella Tramacere (Università Roma Tre, Italy)
This talk presents a distributed view of mental systems, challenging both reductionist and overly broad emergentist frameworks. Drawing on key cases from cognitive neuroscience, I propose a diachronic and integrative perspective where causal control over system behavior is dynamically distributed across internal and external components. Mental phenomena arise through continuous interactions across components and processes, dissolving strict ontic boundaries between levels and challenging distinctions between internal and external influences. Methodologically, this view encourages interdisciplinary approaches that incorporate temporal, environmental, and developmental dimensions of living systems, while also calling for criteria for defining what constitutes a mental system.
Advanced Methods in Nonlinear Time Series Analysis
Julia Mindlin (University of Leipzig, Germany)
This course provides an in-depth exploration of advanced techniques in nonlinear time series analysis. A core component of the course covers time delay embeddings, based on Takens’ theorem, for reconstructing phase space dynamics from scalar observations. We introduce methods for selecting optimal embedding parameters, and imparticular work with an implementation of the false neighbors method, ensuring minimal self-intersections in reconstructed attractors. To validate dynamical models that can reproduce the behaviour of the experimental time series data, we explore topological analysis of periodic orbits through linking and self-linking numbers (LN, SLN), a method for comparing model-generated and observed attractor structures. We then transition to complexity measures, introducing Shannon entropy, disequilibrium metrics, and the Bandt-Pompe permutation entropy framework to quantify information content and system disorder.
The course includes hands-on computational exercises in Python, emphasizing practical implementation of these methods using real-world application from the climate system. We will work examining the Niño-3.4 region's sea surface temperature (SST) variability using daily data reconstruction and spectral filtering methods. We will discuss how to infer appropiate sampling rates and delays for the time delay embedding based on this particular application. By the end, participants will be equipped with the theoretical foundations and coding skills to analyze nonlinear time series in climate and other complex systems. This will allow participants to approach more complex methods (i.e. machine learning) recently proposed to reconstruct phase spaces or find dynamical models from data. Finally, we will provide a short overview of relevant literature to further explore these analysis tools.
Together, but not the same: self-organisation towards emergent collectives
Madalina Sas (Imperial College London, UK)
We are living in a time of social alienation and political division, where many people feel disconnected from the leadership structures who fail to represent their interests; but in the natural world of eusocial insects and social animals, such inefficient social structures would not survive.
In my talk, I will try to show how the kind of self-organising behaviour seen in animals can be tremendously beneficial to humans. I will draw from studies of swarm intelligence in the natural world, starting with how birds flock, and ants vote, and slime moulds solve mazes, and discuss how these behaviours relate to behaviour in humans, by comparison with different frameworks in philosophy, anthropology, and sociology. Then I will present quantitative studies of self-organised behaviour in humans, and relate it to important collective activities such as rituals, protests and marches, art and sport, but also important modes of interaction to foster such collective activities, such as joint action and improvisation.
Finally, I present Synch.Live, a novel participatory experimental technology and collective artistic experience inspired from swarm intelligence. Its goal is to induce collective behaviour in human groups, study the conditions required for self-organisation to emerge, as well as the balance between individual and collective.
Large-scale integration of perceptual and predictive information is encoded by non-oscillatory neural dynamics
Andres Canales (University of Cambridge, UK)
The brain is characterized by extensive recurrent connectivity within and between areas. This recurrent connectivity enables various patterns of arrhythmic (non-oscillatory) and rhythmic (oscillatory) neural activity that are temporally coordinated between regions. What role do these distinct dynamics play in the large-scale integration of perceptual and predictive information? In this talk, I will discuss how information theory combined with EEG , ECoG, and computational modelling can help us uncover large-scale neural patterns of non-oscillatory activity during perception and prediction. In the first series of studies, I will show how non-oscillatory rather than oscillatory dynamics encode perceptual and predictive information across sensory modalities in different species. In the second part, I will discuss how non-oscillatory dynamics encode synergistic (complementary) rather than redundant (common) information between brain areas during visual and auditory predictive processing. These empirical and theoretical observations will provide new insights into the functional role of non-oscillatory dynamics during the large-scale integration of perceptual and predictive information.
Are human coordination dynamics embedded in social network structures?
Ivana Kovanlinka (Technical University of Denmark, Denmark)
Coordinated dynamics are ubiquitous in nature, manifesting in phenomena ranging from coordinated firing of pacemaker cells, to fireflies flashing in unison, to the synchronized clapping of an engaged audience. A rich body of literature within the cognitive sciences and social neuroscience has established that interpersonal coordination between people’s movement and bodily signals both emerges spontaneously and can be negotiated intentionally. However, the factors that perturb or shape these dynamics – and their functional role – remain less well understood. In this talk, I will present empirical studies showing how coordination dynamics between people’s bodily signals (e.g., movement, physiological) are modulated by both bottom-up and top-down mechanisms, including lower-level task-related and higher-level social factors such as social bonds and social network structures. These factors influence whether people spontaneously integrate or segregate self from others, resulting in e.g., mutually-adaptive coordination vs. leader-follower or uncoupled dynamics. By linking local coordination patterns to broader social structures, this work shows how social network properties are embodied in interpersonal coordination dynamics, and vice-versa how emergent interaction dynamics in dyads and groups may contribute to the organization of human social systems.Emergence in the Life Sciences
Lauren Ross (University of California, Irvine, USA)
This talk examines emergence in the life sciences, with a focus on examples of paradigmatic cases, how these cases are explained in scientific contexts, and what makes them distinct from non-emergent examples. This work suggests that clear conceptions of emergence are supported by keeping considerations of (i) physicalism distinct from (ii) scientific explanation, including the principles, reasoning, and guidelines that support such explanations. In this analysis, emergence is related to systems science approaches, complexity considerations, and it is contrasted with common reductionist frameworks in science.
Emergence through Dynamical Closure
Borjan Milinkovic (NeuroPSI, France)
Emergence manifests in a variety of forms. Contemporary operational approaches reflect this diversity by employing a range of distinct formalisms. One such approach focuses on identifying closure within subsystems of a larger dynamical system composed of many interacting components. This session introduces the concept of closure and examines how it has been formalised across various domains. We will explore operational closure in biological systems, information closure in information-theoretic systems, and dynamical closure in both dynamical and statistical systems. Emphasis will be placed on drawing conceptual connections between these often disparate theoretical frameworks. The session will further speculate on how regularities between components enable closure to coarse-grain the sub-processes that define a system, thereby offering a dynamics-first perspective of emergent phenomena. We conclude by considering how information theory may provide a promising foundation for a general theory of emergence via dynamical closure.
Tipping points in the assembly and disassembly of life across scales of biological organization
Jordi Bascompte (University of Zurich, Switzerland)
I will start my talk by presenting a brief account of the history of life on earth highlighting two major evolutionary transitions: the origin of the complex cell, which paved the path towards the posterior evolution of multicellular organisms, and the rise of complex ecological communities. I will argue that the origin of the eukaryotic cell can be understood in terms of an algorithmic evolutionary phase transition. Protein-based gene regulation faced a limit as finding longer proteins become computationally unfeasible. Evolution escaped such a limit to complexity---while maintaining a conserved mechanism of gene growth---by shifting to a new type of genetic regulation based on non-coding DNA sequences. I will then move to a larger scale of biological organization, that of ecological communities, by focusing on their resilience in the face of anthropogenic influences. I will describe ecosystem shifts to alternative states, mechanisms that can delay such shifts by increasing community resilience, and to what degree such shifts can be predicted by generic early-warning signals. Using these two examples, I will advocate that both the assembly and the disassembly of life on earth follow sudden transitions with common statistical properties.
On the Emergence of a Hierarchy of Timescales in the Brain: Reconciling Criticality with Temporal Processing
Leonardo Gollo (IFISC, Spain)
The brain exhibits a hierarchy of timescales, from rapid neural responses to slower cognitive processes, which we propose arises from regions operating at varying distances from criticality. Slower regions are positioned closer to the critical point, where critical slowing down prolongs recovery times, while faster regions function in subcritical regimes, allowing rapid activity dissipation. This structured distribution of critical and subcritical dynamics enables the brain to balance sensitivity and stability, supporting both adaptive responsiveness and robust internal processing. By integrating nonlinear dynamics and criticality theory, our framework provides a mechanistic explanation for the emergence of hierarchical timescales, offering new insights into neural computation, cognition, and the brain's functional organization, with implications for understanding neurological disorders linked to disrupted temporal processing.
Emergence in language: Theoretical models and data analysis
David Sánchez (IFISC, Spain)
Language is a complex adaptive system where individual interactions, cognitive constraints, and social structures collectively give rise to new linguistic patterns. In this talk I will argue that this emergence in language can be examined from a twofold perspective. Whereas intrinsic emergence explains how language internally evolves, extrinsic emergence explains how language shapes and is shaped by speech communities. The former is useful to understand patterns of word usage, the emergence of syntactic rules through grammaticalization and semantinc drifts due to sociocultural changes. The latter is key to the development of speech communities that share linguistic norms or show departures from standard rules (dialects, sociolects) and the vitality of language depending of its prestige the speakers' linguistic ideology. I will emphasize how modern tools such as the analysis of social media large datasets sheds light on the mutual influence between micro-level changes and macro-level variation.