>> DIEP seminars and talks - 2024
DIEP is a broad interdisciplinary endeavour with the aim of bringing scientists together and fostering collaborations in order to progress the science of emergence. For this purpose, DIEP organises a wide variety of events, ranging from brainstorming sessions, community building events, workshops, conferences, public lectures, technical talks and cocktail parties. Regular events include workshops and DIEP seminars (see also the events page).
To register for the DIEP seminars taking place every Thursday please fill out the form here. See full calendar of events here.
See recordings of all talks here. To access older seminars you can check the seminar archives of 2018/2019, 2021, 2022, and 2023.
>> DIEP Seminar: Sune Lehmann (TU Denmark)
Using sequences of life-events to predict human lives | 11am, 28th of November 2024
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Here we represent human lives in a way that shares structural similarity to language, and we exploit this similarity to adapt natural language processing techniques to examine the evolution and predictability of human lives based on detailed event sequences. We do this by drawing on a comprehensive registry dataset, which is available for Denmark across several years, and that includes information about life-events related to health, education, occupation, income, address and working hours, recorded with day-to-day resolution. We create embeddings of life-events in a single vector space, showing that this embedding space is robust and highly structured. Our models allow us to predict diverse outcomes ranging from early mortality to personality nuances, outperforming state-of-the-art models by a wide margin. Using methods for interpreting deep learning models, we probe the algorithm to understand the factors that enable our predictions. Our framework allows researchers to discover potential mechanisms that impact life outcomes as well as the associated possibilities for personalized interventions.
>> DIEP Seminar: Brennen Fagan (University of York)
Maternal transmission as a microbial symbiont sieve | 11am, 21st of November 2024
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With over 200 million years to evolve and theoretical evolutionary stability, why don't strictly monogamous mammals exhibit biparental lactation? Usually, the answers are around certainty of paternity, competition for mates, or sexual selection, but these are usually equally applicable to biparental care as a whole, which does evolve. In this talk, I will use a simple model to suggest an additional reason why biparental lactation specifically might not evolve: transmission of the gut microbiome. By linking theoretical microbiomes to the fitness of the host, we can examine how the population of hosts changes when new microbiomes invade.We observe that biparental transmission leaves the door open for microbiomes that decrease the fitness of the host while uniparental transmission sieves out the microbiomes detrimental to the host. Allowing for environmental transmission provides conditions for uniparental transmission to evolve to a state of fixation. Finally, I will highlight path dependence and biological realities.
>> DIEP Seminar: Chaitanya Gokhale (University of Würzburg)
Pan narrans | Collective narratives and cooperation | 11am, 11th of November 2024
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Human cooperation has always been underpinned by shared beliefs—mythologies, ideologies, and cultural narratives- creating common ground among diverse individuals. While traditional views hold that these narratives must carry explicit moral imperatives to foster prosocial behaviour, our research demonstrates that even arbitrary beliefs can effectively catalyse cooperation. Through an evolutionary model, we reveal how these beliefs operate as coordination devices, fostering trust by aligning individual actions toward shared goals. Such narratives, even when morally neutral, transform self-interested actors into a cohesive group, suggesting that the power of collective imagination is rooted first in its ability to unite and, perhaps later, define morality. Extending this inquiry, we also find that the structure of social networks impacts belief-driven cooperation, with densely clustered networks accelerating the spread of trust and collective action. These insights suggest that narrative and social connectivity are vital to sustaining cooperation, reflecting a deeply ingrained human tendency to seek common ground through shared stories.
>> DIEP Seminar: Marilyn Gatica (Northeastern University London)
High-Order Functional Interactions in Health and Therapy: Aging and Transcranial Ultrasound Stimulation | 11am, 7th of November 2024
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The brain’s interdependencies can be analyzed from structural and functional perspectives. Structural connectivity (SC) typically examines white-matter fibers connecting different regions, while functional connectivity (FC) involves statistical interactions. While SC is inherently pairwise, FC isn’t limited to pairwise relationships; however, most FC analyses still focus on pairwise statistics, often overlooking higher-order interactions. In this talk, Marilyn will discuss new whole-brain computational models developed using MRI data to explore these interactions in two contexts: healthy aging and TUS. Her findings reveal that non-linear changes in the structural connectome can explain variations in high-order functional interactions between age groups. Additionally, Marilyn will discuss the high-order effects of TUS in targeting different brain areas.
>> DIEP Seminar: Jordi Piñero (Complex Systems Lab UPF)
Information Bounds Production in Replicator Systems | 11am, 31st of October 2024
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We investigate the relationship between information and production in flow reactors containing self-replicating molecules under environmental fluctuations. By analyzing productivity patterns, we reveal how information costs –analogous to learning and prediction accuracy– impact the system’s efficiency in production. We connect these findings to information-theoretic concepts such as side-information and Kelly betting.
We introduce a plausible model based on synthetic molecular replicators, demonstrating intrinsic memory processing within cyclic environments. Our framework yields empirically accessible observables, enabling experimental measurement of information flows in chemical systems. Our results suggest that information and memory play a fundamental role in guiding the evolution of self-replicating systems.
>> DIEP Seminar: Mariangeles Serrano (University of Barcelona)
Renormalization of Complex Networks in Latent Space | 11am, 24th of October 2024
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The renormalization group in physics provides a rigorous approach to analyzing systems across different length scales. However, applying this method to complex networks is challenging due to the small-world property, which introduces correlations across coexisting scales. To address this, we have developed renormalization methods within the framework of network geometry, enabling the exploration of both weighted and unweighted real networks at different resolutions. This technique, based on progressively coarse-grained and rescaled network maps in hyperbolic space, has revealed multiscale self-similarity as a common symmetry in real networks. Additionally, this finding explains the self-similarity observed in anatomical multiscale reconstructions of human brain connectomes. Self-similarity is also evident in the growth patterns of certain networked systems, whose evolution can be effectively modeled by a reverse renormalization process. Practical applications of the self-similar multiscale unfolding of real networks include producing scaled replicas, which can be used in a variety of downstream tasks, such as studying processes where network size is relevant.
>> DIEP Seminar: Rob de Boer (Utrecht University)
How Herd Immunity Mitigated a Deadly Second Wave of COVID-19 in Manaus | 11am, 17th of October 2024
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Manaus, Brazil, experienced two severe waves of COVID-19 deaths due to two different strains of SARS-CoV-2. Since most individuals were infected during the first wave, and the second strain nevertheless increased the mortality rates, there seems to be little cross-immunity between the two strains. Brazil has excellent public data bases providing the number of deaths due to COVID-19, and the fraction of the population having antibodies. We model this data to study why the second wave was smaller but deadlier using MCMC methods. We find that cross-immunity indeed provides little protection to reinfection, but provides excellent protection against severe disease. Hence most of the deaths during the second wave are due to primary infections caused by a large wave of reinfected individuals that are themselves well protected. We demonstrate that the first wave elicited such a form of herd immunity by artificially blocking the first wave, and observing that this intervention markedly increases the death rate during the second wave.
>> DIEP Seminar: Fernando Rosas (University of Sussex)
The Many Faces of Emergence: Formalisms, Opportunities, and Challenges | 9:30am, 10th of October 2024
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Emergence is one of the most intriguing aspects of complex systems and has long been a subject of debate. In this talk, I will present a pluralistic and pragmatic approach to emergence, embracing multiple perspectives while emphasizing the need for methods to formulate testable hypotheses and rigorous procedures for their verification. This framework will be demonstrated through two operationalizations of emergence: (i) self-contained levels of description and (ii) synergistic interactions between system components.
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>> DIEP Seminar: Christian Bick (Vrije Universiteit Amsterdam)
Coupled Oscillators, Synchronization, and Higher-Order Phase Interactions | 11am, 3rd of October 2024
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Synchronization is a fascinating effect of the interaction between coupled oscillatory units and is ubiquitous in physical systems. If the coupling between units is sufficiently weak, the reduction to phase variables is a useful dimensionality reduction technique to analyze the synchronization dynamics. Here we discuss how non-pairwise 'higher-order' phase interactions arise in higher-order phase reductions, how they depend on the shape of the limit cycles and the underlying network structure, and the information they give on synchronization.
>> DIEP Seminar: Tuan Pham (University of Amsterdam)
Irreversibility in Non-reciprocal Neural Networks | 11am, 26th of September 2024
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How is entropy production, a measure of time irreversibility, of a complex system with emergent chaotic behaviour controlled by the heterogeneity in the non-reciprocal interactions among its elements? Despite the challenge in computing entropy production within a microscopic theory, the importance of entropy production for the brain’s cognition as recently demonstrated, greatly motivates theoretical work on this question. In this talk, we address this question using a classic model of random recurrent neural networks that undergoes a dynamical transition from quiescence to chaos at a critical heterogeneity level. By obtaining an exact analytical expression for the averaged entropy production rate, we show how this quantity becomes a constant at the onset of chaos while changing its functional form upon crossing this point. Our work provides the first step to understand not only non-equilibrium phase transitions without broken symmetry but also the connection between dynamics and thermodynamics of complex living systems such as the brain.
>> DIEP Seminar: Andrea Luppi (Cambridge University)
Modelling How Brain Function Emerges from Network Architecture in Space and Time | 11am, 19th of September 2024
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Disentangling how cognitive functions emerge from the interplay of brain dynamics and network architecture is among the major challenges faced by neuroscientists. Addressing these complex challenges requires a concerted integration of theory and data. In this talk, I will show how we can investigate the spatial and temporal dimensions of structure-function relationships in the brain, by combining the formal tools of network science with pharmacological and pathological perturbations.
I will show how perturbations of consciousness induce convergent reconfigurations of the brain’s unimodal-transmodal functional architecture. However, loss of consciousness increases structural constraints on brain dynamics across scales, whereas psychedelics decouple brain function from anatomy. Computational models provide a formal way to integrate our understanding.
>> DIEP Seminar: Frederik de Laender (University of Namur)
Persistence and Coexistence of pecies in Spatial Networks | 11am, 12th of September 2024
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A classic result in community ecology is that dispersal between patches (e.g. islands, forest fragments) promotes local biodiversity in those patches. However, the mechanisms causing this result are not well understood. Available patch-occupancy models predict the fraction of patches a given species occupies, but either only consider a single species, or imply extinction of all species across all patches in absence of dispersal. Available simulation models are more realistic but have been impossible to mathematically analyse beyond species pairs, making it unclear how general their simulation results really are. During my talk, I will present new results that shed light on the effect of dispersal on local biodiversity. I will show how the assumption of small dispersal leads to an analytical treatment of an otherwise impalpable model. A first analysis focuses on the species level, showing how dispersal, local species interactions, and the size of the spatial network (i.e. the number of patches) jointly influence persistence. Interestingly, the effect of local interactions is fully captured by the focal species invasion growth rate, a main variable of interest in modern coexistence theory. A second analysis focus on the community level, introducing the idea of community-level patch occupancy: the fraction of patches occupied by all species and thus the community as a whole. I show how patch occupancy is intimately linked to feasibility, another key concept in coexistence theory. I end by showing how our predictions of patch occupancy match simulations, relaxing all our assumptions.
>> DIEP Seminar: Monique de Jager (Utrecht University)
Solving the SLoSS Debate: Scale-Dependent Effects of Habitat Fragmentation on Biodiversity Loss | 11am, 5th of September 2024
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Should what is left of nature be contained in a Single Large or Several Small (SLoSS) areas? What would minimize the severe impact of habitat destruction on biodiversity loss is much debated, mainly because studies generally focus on different spatial and temporal scales. Using a semi-spatially explicit, (near-)neutral, individual-based model, we investigate the effects of fragmentation on biodiversity loss at two spatial (landscape- versus subcommunity level) and two temporal scales (static versus dynamic effects). Our results show that the role of spatial configuration of habitat destruction depends on when and at what scale we measure biodiversity loss. When considering the more realistic assumption that species differ in dispersal capacity, differences between spatial configurations are amplified. Our results indicate that the spatial configuration of habitat loss needs to be considered when evaluating the risks of further habitat destruction.
>> DIEP Seminar: Stefania Sardellitti (Universitas Mercatorum)
Topological Signal Processing with Applications to Brain Networks | 11am, 27th of June 2024
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Recently, we are witnessing a vibrant and fast-growing interest in the signal processing and machine learning communities in extracting and analyzing multiway relationships from complex data. Topological Signal processing (TSP) is an emerging and powerful framework combining signal processing and topological tools to represent, analyse and process signals defined over topological domains. Topological domains, such as simplicial and cell complexes, enable the extraction and capture of higher-order relationships among data, overcoming the limitations of graph-based representations that only extract dyadic relationships between pairs of vertices. In this talk, I will present methods for filtering, sampling and estimation of signals defined over simplicial and cell complexes. These topological spaces exhibit a rich hierarchical structure represented through a powerful algebraic framework that allows learning of global properties of the data by using local operators. Efficient methods to learn the structure of the topological domain from data will be presented, with a focus on interesting applications to brain networks. In brain networks, a fundamental issue is learning multiway relationships between groups, such as neurons or brain regions, that encode electrical or chemical coupling. Using TSP tools for identifying cycles and cavities can provide useful insights into the complex interactions and structures that underlie brain functions and diseases.
>> DIEP Seminar: Subodh Patil (Leiden University)
It's a Small World! (and How Complex Networks Help Us Understand Why) | 11am, 20th of June 2024
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Networks are a useful mathematical abstraction for any system where the relations between any two elements (or agents) are as significant to our understanding as the elements themselves. A remarkable feature emerges in a wide class of networks: that on average, one can hop from any two elements with a remarkably small number of hops -- the so-called small world property. In this talk I will review this idea illustrated with some familiar examples and present a novel mean field method to study this, and other properties of complex networks where heterogeneity of interactions and disorder and may be present. Each italicized term in this abstract will be explained in simple terms that require little to no prior or formal knowledge to accompany more formal aspects of the discussion. I will conclude with some potential applications of the work presented towards social, epidemiological, and biological systems.
>> DIEP Seminar: Matteo Marsili (ICTP)
What Is Abstraction? | 11am, 13th of June 2024
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Deep neural networks develop representations of the data they are trained with, with a level of abstraction that increases with depth, in a similar way as our brain does. But what is abstraction? Abstraction is qualitatively assessed by looking at the features of the data to which internal nodes respond to. Can the level of abstraction be quantified in terms of the statistical properties of the activity of internal nodes without reference to what is represented (i.e. the data)? We address these questions analysing the internal representation of deep belief networks trained on benchmark datasets.
>> DIEP Seminar: Tobias Stark (Utrecht University)
How Dual Identifiers Affect Interethnic Relations in Social Networks | 11am, 30th of May 2024
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Improving relations between ethnic minority and ethnic majority groups is one of the most pressing needs in modern societies. During this talk, I will present research from my group in which we test a new theory: that such relations can be improved by descendants of immigrants who identify with both the ethnic group of their parents and the national majority group, because these dual identifiers can create social bridges between communities. However, not all dual identifiers are recognized as such by others, as many descendants of immigrants experience being labeled, for instance, "Turks" or "Moroccans" instead of (also) "Dutch". This may undermine the amount of bridging that dual identifiers can accomplish. In this talk, I will present a series of studies in which we explored why ethnic majority members do (not) ascribe co-national belonging to descendants of immigrants and how descendants of immigrants can signal their national identification. Our hypothesis is that dual identifiers' relationships with members of both groups are seen as signals of their dual belonging, but that the degree to which these signals are picked up depends on people?s perception of the structure of their social networks. To test this hypothesis, we have developed a novel open-source software that allows collecting data on perceived social networks and the perception of others' ethnic/national belonging.
>> DIEP Seminar: Rak Kim (Utrecht University)
The Complexity of Law and Governance | 11am, 23rd of May 2024
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The concept of complexity has received considerable attention in the study of legal and governance institutions. Its relevance stems from the inherent complexity of both the issues being regulated (e.g., climate change) and the institutional frameworks themselves (e.g., international climate regime). The challenge lies in navigating this dual complexity, or managing complex institutions in order to manage other complex social-ecological systems. This is a relatively new and ongoing area of research, and this talk will provide a broad overview aimed at an interdisciplinary audience who may not be familiar with the specific literature. We will explore various conceptualisations, operationalisations, and explanations of institutional complexity, as well as its political and systemic implications for governance outcomes. We will conclude with a reflection on the potential for interdisciplinary collaboration to enhance our capacity to harness complexity in law and governance.
>> DIEP Seminar: Ginestra Bianconi (Queen Mary University of London)
Topology Shapes Dynamics of Higher-Order Networks | 11am, 2nd of May 2024
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Higher-order networks capture the interactions among two or more nodes and they are raising increasing interest in the study of brain networks. Here we show that higher-order interactions are responsible for new non-linear dynamical processes that cannot be observed in pairwise networks. We reveal how non-linear dynamical processes can be used to learn the topology, by defining Topological Kuramoto model and Topological global synchronization. These critical phenomena capture the synchronization of topological signals, i.e. dynamical signals defined not only on nodes but also on links, triangles and higher-dimensional simplices in simplicial complexes. Moreover, will discuss how the Dirac operator can be used to couple and process topological signal of different dimensions, formulating Dirac signal processing. Finally, we will reveal how non-linear dynamics can shape topology by formulating triadic percolation. In triadic percolation triadic interactions can turn percolation into a fully-fledged dynamical process in which nodes can turn on and off intermittently in a periodic fashion or even chaotically leading to period doubling and a route to chaos of the percolation order parameter. Triadic percolation changes drastically our understanding of percolation and can describe real systems in which the giant component varies significantly in time such as in brain functional networks and in climate. We show that in many situations the statistics is determined by the details of the driving process and does not depend on the specific relaxation dynamics. Simple (homogeneous) driving strategies universally lead to Zipf’s law and exact power laws. Other driving processes results in exponential, Gamma, normal, Weibull, Gompertz, and Pareto distributions. We discuss a number of examples of SRR processes, including fragmentation processes, language formation, cascading and search processes, as well as a classic in physics: inelastical collisions.
>> DIEP Seminar: Wilson Poon (University of Edinburgh)
Active Self Disassembly: Towards a Physics of Death | 11am, 25th of April 2024
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The concept of self-assembly was first invented by two physicists in 1962 to explain the construction of viral capsids. Since then, the idea that biology is self-assembled soft matter has become commonplace. However, biology at all length scales from molecules through cells and organisms to ecosystems also depends vitally on processes of active self-disassembly. Living systems have evolved to use energy to deconstruct part or all of themselves in highly-organised ways, and feedback building blocks to self-assembly processes. Programmed (or regulated) cell death in our tissues is a good example, but death at all levels has been highly-evolved to enable life to function. In this talk, I consider what such a physics of death may look like, report briefly some ongoing work in this direction, and explain why such research may have a vital role to play in the drive towards a more sustainable circular economy.
>> DIEP Seminar: Jacopo Grilli (International Centre for Theoretical Physics)
Competition without contingency and functional convergence | 3pm, 18th of April 2024
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Microbial communities are taxonomically diverse and variable: species presence and their abudances widely fluctuate over time and space, and even across biological replicates in experimental controlled conditions. On the other hand, environmental conditions exert a strong selection on the traits of community members and the function they perform, and similar environmental conditions are expected to correspond to functionally similar communities. The consequence of this environmental selection, together with taxonomic variability, lead to the influental concept of functional redundancy: the same function is performed by many species, so that one may assemble communities with different species but the same functional profile. The centrality of the concept of functional redundancy in microbial ecology does not parallel with a theoretical understanding of its origin. In this talk I will describe the eco-evolutionary dynamics of communities interacting through competition and cross-feeding. I will show that the eco-evolutionary trajectories rapidly converge to a "functional attractor'', characterized by a functional composition uniquely determined by environmental conditions. The taxonomic composition instead follows non-reproducible dynamics, constrained by the conservation of the functional composition. This framework provides a deep theoretical foundation to the concepts of functional robustness and redundancy.
>> DIEP Seminar: Stefan Thurner (Complexity Science Hub Vienna)
Towards a statistics of driven complex systems | 11am, 18th of April 2024
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Most complex systems are not in equilibrium but driven. We argue that driven systems can be understood by so-called sample space reducing (SSR) processes. They provide an intuitive understanding of the origin and ubiquity of fat-tailed distributions in complex systems, power-laws in particular. SSR processes are mathematically simple and offer an alternative to Boltzmann-equation based approaches to non-equilibrium systems. We show that in many situations the statistics is determined by the details of the driving process and does not depend on the specific relaxation dynamics. Simple (homogeneous) driving strategies universally lead to Zipf’s law and exact power laws. Other driving processes results in exponential, Gamma, normal, Weibull, Gompertz, and Pareto distributions. We discuss a number of examples of SRR processes, including fragmentation processes, language formation, cascading and search processes, as well as a classic in physics: inelastical collisions.
>> DIEP Seminar: Harsha Honnappa (Purdue University)
Pathwise Relaxed Optimal Control of Non-Markovian Systems | 11am, 11th of April 2024
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This talk presents a theoretical framework for a definition of differential systems that model reinforcement learning or simulation-based control in continuous time non-Markovian rough environments. The desideratum for such a framework arises, in part, from rare event estimation for non-Markovian stochastic systems in the Friedlin-Wentzell small noise setting for instance. Specifically, I will focus on optimal relaxed control of rough equations (the term relaxed referring to the fact that controls have to be considered as measure valued objects). In a general context, our contribution focuses on a careful definition of the corresponding relaxed Hamilton-Jacobi-Bellman (HJB)-type equation. A substantial part of our endeavor consists in a precise definition of the notion of test function and viscosity solution for the rough relaxed PDE obtained in this framework. Note that this task is often merely sketched in the rough viscosity literature, in spite of the fact that it gives a proper meaning to the differential system at stake. We show that a natural value function solves a rough HJB equation in the viscosity sense. With reinforcement learning in view, our reward functions encompass forms that involve an entropy-type term favoring exploration. I will demonstrate that, in this setting, closed-form expressions for the optimal relaxed control are obtainable.
>> DIEP Seminar: Ricardo Martinez-Garcia (Center for Advanced Systems and Understanding (CASUS), Germany)
Causes and consequences of imperfect coordination in collective behaviors across scales: from microbial aggregates to ungulate migration | 11am, 4th of April 2024
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Collective behaviors, in which many individuals exhibit some degree of behavioral coordination, are frequent in nature and observed across a continuum of scales, from microbial aggregates to ungulate migrations. Intriguingly, however, such coordination is sometimes imperfect, and out-of-sync individuals exist in many of these systems. The roots of such imperfect coordination, and hence the mechanisms underlying the emergence of out-of-sync individuals, will undoubtedly differ across systems. Nevertheless, the occurrence of imperfect coordination across such different systems and scales raises fundamental questions about its causes and consequences. Are ?out-of-sync? individuals merely inevitable byproducts of large-scale coordination attempts, or can they, at least in some systems, be a variable trait that selection can shape with potential ecological consequences? I will address this question by combining empirical data on slime-mold imperfect aggregation and observed patterns of partial migration observed within three ungulate specie. In each of these systems, we find that the number of individuals that do not engage in the collective behavior is unrelated to the total population size, suggesting that a complex individual decision-making process underlies the onset of the collective behavior. Using a minimalistic modeling framework, we propose that imperfectly synchronized collective behaviors are, in fact, a dynamic population partition process that originates from each individual making a stochastic signal-based decision. The parallelisms between these two seemingly different systems suggest that imperfectly synchronized collective behaviors could be critical to understanding social behaviors and ecological dynamics across scales.
>> DIEP Seminar: Swinda Falkena (Utrecht University)
From coupled Earth System Models to a conceptual climate model and back; the case of the subpolar gyre | 11am, 14th of March 2024
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Conceptual climate models are based on a physical understanding of the system of interest. What processes are at play? What are the interactions between different components? The results of studying a conceptual climate model in isolation, knowing the processes are reasonably realistic, are often used to infer aspects of the real system. The question is how well these results extend from a relatively simple system to a highly complex one. In this talk I will discuss this question using the example of the subpolar gyre. The subpolar gyre in the North-Atlantic ocean is a key area for convection, which contributes to the Atlantic Meridional Overturning Circulation (AMOC). The circulation itself is wind-driven, but the convection in its center is the result of different positive and negative feedback mechanisms. There exists a conceptual model representing the dynamics of this convection, which is based on studies of a limited number of coupled earth system models (ESMs). I will discuss how well the dynamics this model describes are represented in a large set of ESMs. Which parts of the dynamics are accurately captured? Which are not? And what are the consequences if we want to extend results from the conceptual model to the real system? I will end with an outlook and discussion of next steps.
>> DIEP Seminar: Andrey Bagrov (Radboud University Nijmegen)
Multi-scale structural complexity of natural patterns | 11am, 7th of March 2024
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Complexity of a pattern or a system is a very intuitive concept that, at the same time, is very elusive when one attempts to give a formal definition of it. In nature, a distinctive feature of complex systems is the multi-level hierarchy of organization, with different levels being drastically different from each other (think, e.g., of the levels of organelles, cells, tissues, and organs in a human body). In this talk, I will review the measure of structural complexity of complex systems based on the idea of their inter-scale self-dissimilarity introduced by us a few years ago [Bagrov et. al, 2020)]. I will show how this measure can be used to quantify perceptive psychological complexity of patterns, to describe phase transitions in classical and quantum systems, and to address the problem of verification of quantum many-body states. If time allows, I will cover some of recent applications of this concept in diverse contexts such as time series analysis and quantum simulators.
>> DIEP Seminar: Frederike Oetker (University of Amsterdam)
Navigating Cocaine Networks: Computational Tools and Data-Driven Insights | 11am, 29th of February 2024
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We present an innovative approach to understanding and intervening in criminal cocaine networks, employing three validated functional computational models created using Agent-Based Models (ABMs). These models aim to simulate network dynamics post-node removal, intervention strategies, and a large-scale digital twin of the criminal cocaine network in the Netherlands. To process law enforcement data inputs, the FREIDA (Framework for Expert-Informed Data-driven Agent-based models) framework facilitates the creation of validated computational models through integrating qualitative and quantitative data input such as case files and arrest data bases to understand emergent behaviour in a criminal context. We enrich the model by applying a tie strength classification to investigate network composition and potential link prediction between agents and keeping in mind the value network of cocaine trade.
An interactive dashboard is being developed for end-user manipulation of data and models.
>> DIEP Seminar: Bernadette Stolz (EPFL)
Applications of global and local persistent homology for the shape and classification of biological data | 11am, 22th of February 2024
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Topological data analysis (TDA) is an emerging mathematical field that uses topological and geometric approaches to quantify the “shape” of data. In the first part of this talk, I will showcase how persistent homology, a method from TDA, can be used to spatially characterise structural abnormality in tumour blood vessel networks reconstructed from experimental data. More specifically, I will show that the number of vessel loops and their spatial distribution in these networks change over time when tumours undergo treatment with vascular targeting agents and radiation therapy. I will also show what insight TDA can give when applied to synthetic data generated from mathematical models of tumour-induced vascular growth. In the second part of the talk, I will demonstrate applications of local persistent homology. I will show how local persistent homology can be used to select landmarks from large and noisy data sets. In contrast to existing methods, this subsampling process is robust to outliers and is developed specifically as a preprocessing step for persistent homology. Based on similar ideas, I will introduce a novel method that can detect geometric anomalies, such as intersections or boundaries, in point cloud data sampled from intersecting surfaces. This detection is based on the computation of persistent homology in local annular neighbourhoods around points and is less sensitive to the size of the local neighbourhood and surface curvature than local principal component analysis.
>> DIEP Seminar: Els Weinans (Utrecht University)
Drivers of Polarization and consensus | 11am, 15th of February 2024
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Our ability to deal with external changes such as climate change, pandemics, and geopolitical developments, does not solely depend on the development of new ideas or technologies, but also on our willingness to adopt new ideas. Opinion dynamics models are one way to explore how people may adopt new ideas. In this talk, I will introduce a recently published model on opinion dynamics that helps to explore the possible pathways towards consensus and polarization. We find that under most parameter settings, both consensus as well as polarization and an in-between co-existence state are possible outcomes of the model. Sensitivity analysis reveal that the most important determinant of model outcome, is the amount of stochasticity, where consensus is found for intermediate levels of stochasticity.
>> DIEP Seminar: Irene Ferri (University of Barcelona)
Opinion Modeling from Statistical Physics | 2pm, 1st of February 2024
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In an increasingly globalized world, society confronts complex challenges with numerous interconnected variables. Statistical physics offers a framework to study human behavior by simplifying general situations to the interplay of a few essential parameters. This approach is valuable for comprehending and addressing practical issues, including achieving consensus in areas such as energy production, resource management and poverty reduction. Moreover, it can contribute to the enhancement of deliberative spaces. Our proposal introduces an agent-based three-state opinion model, emphasizing the relationship between neutral and extremist opinions and examining how opinions evolve in tandem with the rearrangement of social ties.
>> DIEP Seminar: Frank Pijpers (University of Amsterdam)
Statistics Netherlands' datasets as a resource for complexity |
11am, 1st of February 2024
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Statistics Netherlands (SN) primary role is as the Dutch National Statistical Institute, and it is known for its output in the form enormous numbers of tables on topics ranging from the potato harvest to Dutch GDP and from demographic change to inflation. Perhaps less widely known is that the microdata, out of which such tables are compiled, can be accessed (for a modest cost to keep IT systems running) by researchers at Dutch universities for research purposes. While some data is survey-based, a lot of datasets are extracted from administrative registers; which means that data about the entire population are available at microlevel. For the purpose of calibrating models for emergent social phenomena especially, such integral datasets are invaluable. In this talk I will discuss some of the benefits and applications of the data, some projects currently being pursued in the field of complexity science by SN researchers and others, but also some pitfalls associated with these datasets.
>> DIEP Seminar: Laura Alessandretti (TU Denmark)
The Dynamics of Online Coordination in the GameStop Stock Surge | 11am, 25th of January 2024
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In this talk, I will present a systematic investigation of the GameStop stock surge, a paradigmatic example of collective action facilitated by social media platforms. The focal point of this analysis is the r/wallstreetbets community on Reddit, which contributed a dramatic financial event through an unprecedented form of online coordination. Using a data driven approach, I will demonstrate the key role played by a committed minority within the online community and that of the community's social identity as events unfolded. I will unveil the linkage between online discourse and market movements, revealing distinct phases of activity impacting the stock behaviour. The presentation aims to provide a nuanced understanding of the interplay between social media and financial markets, offering insights into the emergent collective actions in digital spaces and their implications for market behavior.
>> DIEP Seminar: Tommaso Gili (IMT School for Advanced Studies)
Laplacian Renormalization Group for heterogeneous networks | 11am, 18th of January 2024
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Complex networks usually exhibit a rich architecture organized over multiple intertwined scales. Information pathways are expected to pervade these scales, reflecting structural insights not manifested from network topology analyses. Moreover, small-world effects correlate with network hierarchies, complicating identifying coexisting mesoscopic structures and functional cores. To shed further light on these issues, we present a communicability analysis of effective information pathways throughout complex networks based on information diffusion. This leads us to formulate a new renormalization group scheme for heterogeneous networks. The Renormalization Group is the cornerstone of the modern theory of universality and phase transitions, a powerful tool to scrutinize symmetries and organizational scales in dynamical systems. However, its network counterpart is particularly challenging due to correlations between intertwined scales. The Laplacian RG picture for complex networks defines the supernodes concept à la Kadanoff and the equivalent momentum space procedure à la Wilson for graphs. A direct application of the Laplacian Renormalization Group (LRG) framework is the multi-scale Laplacian (MSL) community detection algorithm. Based on inter-node communicability, our definition provides a unifying framework for multiple partitioning measures.
>> DIEP Seminar: Daniel Miranda (Federal University of Pernambuco)
Phenomenological renormalization group analysis of cortical spiking data | 10am, 21st of September 2023
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The critical brain hypothesis has emerged in the last decades as a fruitful theoretical framework for understanding collective neuronal phenomena. Lending support to the idea that the brain operates near a phase transition, Beggs and Plenz were the first to report experimentally recorded neuronal avalanches, whose distributions coincide with the mean-field directed percolation (DP) universality class, which comprises a variety of models in which a phase transition occurs between an absorbing (silent) and an active phase. However, this hypothesis is highly debated, as neuronal avalanches analyses and other common statistical mechanics tools may struggle with challenges ubiquitous in living systems, such as subsampling, long range correlations and the absence of an explicit model for the complete neuronal dynamics. In this context, Meshulam et al. recently proposed a phenomenological renormalization group (PRG) method to deal with neural networks typical long range interactions with a model independent analysis. The procedure consists of repeatedly manipulating the data, obtaining an increasingly coarse-grained description of the activity after each iteration. Under a critical regime, non-trivial correlations and scale-free behavior should be unveiled as we simplify our description. This can be inferred from a series of statistical features of the data, which lead us to different scaling relations. Here, we apply this phenomenological renormalization group (PRG) in different experimental setups. Additionally, we investigate how the scaling exponents found via PRG behave as we parse our data by its coefficient of variation (CV); this measurement has appeared in recent literature as a means of tracking different cortical states through spiking variability.
>> DIEP Seminar: Ro Jefferson (Utrecht University)
Physics ∩ Machine Learning | 11am, 14th of September 2023
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Machine learning has become both powerful and ubiquitous, but remains a black box whose internal workings are still largely unclear. In this talk, I will discuss some interesting connections between ideas in physics (in particular QFT and the renormalization group) and deep neural networks in particular, which collectively motivate a physics-based approach towards a theory of deep learning.
