>> DIEP seminars and talks - 2025
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, 2023, and 2024.
>> DIEP Seminar: Jan Korbel (Complexity Science Hub Vienna)
From Spins to Society: Modeling Collective Social Behavior with Statistical Physics | 11am, 17th of April 2025
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Sociophysics is an interdisciplinary field that applies methods from statistical physics to understand collective social phenomena, such as the emergence of polarization in societies. Two key mechanisms often cited as drivers of social dynamics are homophily—the tendency to form friendly ties with like-minded individuals—and social balance—the overrepresentation of triadic relationships where either all three connections are friendly or one is friendly, while the remaining two are hostile. These ideas are captured by the classic sayings:
"Birds of a feather flock together",
"The friend of my friend is my friend; the enemy of my enemy is my friend."
In this talk, I will present recent results showing how these two principles can be jointly modeled using tools from statistical physics. I begin by introducing a model inspired by the Ising model, in which homophilic interactions naturally give rise to social balance as an emergent property. I then show how this model can be extended to explore various forms of collective behavior, focusing in particular on the distribution of group sizes in social networks. I also examine how the topological features of the underlying network—such as node degree—relate to the rise of polarization. Finally, I will discuss how external influences, such as political or media campaigns, can be incorporated into the model to study their impact on opinion dynamics.
>> DIEP Seminar: Giovanni Petri (Northeastern University London)
Renormalization and Higher-Order Interactions: Bridging Structure and Dynamics in Complex Systems | 11am, 10th of April 2025
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In most biological systems, active motion is not a continuous, uninterrupted process but an intermittent one, where displacements occur in bursts. From bacteria to sheep, biological entities switch their active motion on and off. The transport properties of these systems are governed by the underlying decision-making mechanisms that control movement. Moreover, collective motion, from an initially static group, requires all members to transition to an active state. This transition propagates as activation waves, ultimately leading to coordinated group movement. In this talk, Fernando Peruani will introduce a general theoretical framework to explain (i) the emergence of optimal transport in bacteria, as well as (ii) behavioral synchronization, collective intelligence, and criticality in sheep.
>> DIEP Seminar: Bert Kappen (Radboud University)
Stochastic Optimal Control of Open Quantum Systems | 11am, 3rd of April 2025
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We address the generic problem of optimal quantum state preparation for open quantum systems. It is well known that open quantum systems can be simulated by quantum trajectories described by a stochastic Schrödinger equation. In this context, the state preparation becomes a stochastic optimal control (SOC) problem. The latter requires the solution of the Hamilton-Jacobi-Bellman equation, which is, in general, challenging to solve. A notable exception are the so-called path integral (PI) control problems, for which one can estimate the optimal control solution by direct sampling of the cost objective. In this work, we derive a class of quantum state preparation problems that are amenable to PI control techniques, and propose a corresponding algorithm, which we call Quantum Diffusion Control (QDC). Unlike conventional quantum control algorithms, QDC avoids computing gradients of the cost function to determine the optimal control. Instead, it employs adaptive importance sampling, a technique where the controls are iteratively improved based on global averages over quantum trajectories. We also demonstrate that QDC, used as an annealer in the environmental coupling strength, finds high accuracy solutions for unitary (noiseless) quantum control problems. We further discuss the implementation of this technique on quantum hardware. We illustrate the effectiveness of our approach through examples of open-loop control for single- and multi-qubit systems.
>> DIEP Seminar: Fernando Peruani (Paris Cergy University)
Statistical Mechanics of Intelligent Active Matter: Optimal Transport, Synchronization, and Criticality in Bacteria and Sheep Herds | 11am, 27th of March 2025
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In most biological systems, active motion is not a continuous, uninterrupted process but an intermittent one, where displacements occur in bursts. From bacteria to sheep, biological entities switch their active motion on and off. The transport properties of these systems are governed by the underlying decision-making mechanisms that control movement. Moreover, collective motion, from an initially static group, requires all members to transition to an active state. This transition propagates as activation waves, ultimately leading to coordinated group movement. In this talk, Fernando Peruani will introduce a general theoretical framework to explain (i) the emergence of optimal transport in bacteria, as well as (ii) behavioral synchronization, collective intelligence, and criticality in sheep.
>> DIEP Seminar: Fernando Nobrega Santos (University of Amsterdam)
Independent Scaling Exponents in the Brain | 11am, 20th of March 2025
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We apply the phenomenological renormalization group to resting-state fMRI time series of brain activity in a large population. By recursively coarse-graining the data, we compute scaling exponents for the series variance, log probability of silence, and largest covariance eigenvalue. The exponents clearly exhibit linear interdependencies, which we derive analytically in a mean-field approach. We find a significant correlation of exponent values with the gray matter volume and cognitive performance. Akin to scaling relations near critical points in thermodynamics, our findings suggest scaling interdependencies are intrinsic to brain organization and may also exist in other complex systems.
>> DIEP Seminar: Matteo Capucci (Advanced Research and Invention Agency)
An Elementary Account of the Internal Model Principle | 11am, 13th of March 2025
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During this session, Capucci will talk about recent work with Baltieri, Biehl and Virgo [1] on a categorical account of the classical 'internal model principle' from control theory and cybernetics in a broader sense. The aim is to distill the mathematical content of such an informal principle, following previous work of Wonham and Hepburn. In the talk Capucci will only use elementary mathematical notions and thus should be accessible to an audience acquainted with the basic vocabulary of sets and dynamical systems.
[1] Baltieri, Biehl, C., Virgo, "A Bayesian Interpretation of the Internal Model Principle", (preprint), 2025, URL: http://arxiv.org/abs/2503.00511
>> DIEP Seminar: Gemma De les Cover (Universitat Pompeu Fabra)
Universality in Physics, Computer Science and Beyond | 11am, 6th of March 2025
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During the session, Gemma De Ies Coves will consider notions of universality in physics, computer science and others, and will examine their similarities, for example via a framework or by casting spin models as formal languages. She will also consider their relation to notions of unreachability such as undecidability and uncomputability.
>> DIEP Seminar: Abel Jansma (University of Amsterdam)
The Mereology of Higher-Order Interactions | 11am, 27th of February 2025
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A mereology is a partial order that describes a hierarchy of scales, and it turns out that committing to a mereology fixes all higher-order interactions through the Möbius inversion theorem. Abel will first show that this procedure reproduces many well-known quantities from physics, biology, chemistry, game theory, and AI. He will then demonstrate how to use the framework to derive new quantities, focusing on decompositions in information theory and interventional causality, and present a new way to calculate renormalised couplings.
>> DIEP Seminar: Ricard Solé (ICREA-Complex Systems Lab, Santa Fe Institute)
Fundamental constraints to the logic of living systems | 11am, 18th of February 2025
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It has been argued that the historical nature of evolution makes it a highly path-dependent process. Under this view, the outcome of evolutionary dynamics could result in a diverse landscape of complex agents with different forms and functions. At the same time, there is ample evidence that convergence and constraints strongly limit the domain of the potential design principles that evolution can achieve. Are these limitations relevant in shaping the fabric of the possible? Here, we argue that fundamental constraints are associated with the logic of living matter. We illustrate this idea by considering the thermodynamic properties of living systems, the linear nature of molecular information, the cellular nature of the building blocks of life, its open-endedness, the threshold nature of computations in cognitive systems, language and the discrete nature of the architecture of ecosystems. In all these examples, we present available evidence and suggest potential avenues towards a well-defined theoretical formulation.
>> DIEP Seminar: Gabriel Coutinho (Federal University of Minas Gerais)
The combinatorics of quantum walks | 11am, 13th of February 2025
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Imagine a walker that can access a finite number of sites and, at each step, moves to a neighboring site with a certain probability. But now, the walker is quantum. What exactly does this mean? In this talk, we will explore how quantum walks serve as the quantum analogue of classical random walks and, more importantly, what the combinatorics of the underlying graph can tell us about their behavior. We will also briefly survey results from various branches of mathematics that contribute to the study of quantum walks and discuss some open problems.
>> DIEP Seminar: Lourens Waldorp (University of Amsterdam)
Mean field theory of the general-spin Ising model | 11am, 6th of February 2025
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In psychology, the Ising model is often used to model pathologies like depression, where symptoms are represented as nodes and their associations as links. However, a limitation of the traditional Ising model is its binary nature, which fails to capture more subtle variations in states. The general-spin Ising model extends this by allowing 2k + 1 spin values: −1, −(k+1)/k, …, 0, 1/k, …, 1. In this talk, we derive the mean field of the general-spin Ising model using the variational principle of Gibbs free energy. Like the standard Ising model, it exhibits spontaneous magnetization, but with a shift depending on the number of categories. Additionally, the hysteresis effect decreases as the number of spin categories increases. Monte Carlo simulations confirm our theoretical results.
>> DIEP Seminar: Alessandro Ingrosso (Radboud University)
Statistical Mechanics of Transfer Learning in the Proportional Limit | 11am, 30th of January 2025
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Transfer learning (TL) is a well-established machine learning technique to boost the generalization performance on a specific (target) task using information gained from a related (source) task, and it crucially depends on the ability of a network to learn useful features. I will present a recent work that leverages analytical progress in the proportional regime of deep learning theory (i.e. the limit where the size of the training set P and the size of the hidden layers N are taken to infinity keeping their ratio P/N finite) to develop a novel statistical mechanics formalism for TL in Bayesian neural networks. I'll show how such single-instance Franz-Parisi formalism can yield an effective theory for TL in one-hidden-layer fully-connected neural networks. Unlike the (lazy-training) infinite-width limit, where TL is ineffective, in the proportional limit TL occurs due to a renormalized source-target kernel that quantifies their relatedness and determines whether TL is beneficial for generalization.
>> DIEP Seminar: David Saad (Aston University)
Control and mitigation of spreading processes | 11am, 23rd of January 2025
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The modern world comprises interlinked networks of contacts between individuals, computing devices and social groups, where infectious diseases, information and opinions propagate through their edges in a probabilistic or deterministic manner via interactions between individual constituents. The spread of information, opinions and marketing material can be modelled and analysed in a similar manner to that of epidemic spreading among humans or animals. To contain and mitigate the spread of infectious diseases one would like to model the spread probabilistically, implement effective prevention and mitigation policies and deploy vaccines in a way that minimises the spread. This is a difficult problem and becomes even harder in the presence of infectious but asymptomatic individual states. In the world of marketing and opinion setting, winners are those who maximise the impact by deploying resource to the most influential available nodes at the right time, occasionally in competition (or collaboration) with adversarial (supportive) spreading processes. These can represent opinion formation by political parties (competitive) or diseases that increase the susceptibility to mutual infections (collaborative). Additionally, spreading processes on different networks may be interlinked, providing additional challenge in their mitigation and an incentive to share resources. I will explain the modelling of epidemic spreading processes and present the probabilistic analytical framework for impact maximisation/minimisation we have developed, addressing the questions of vaccine (budget) deployment and spreading maximisation in single and competitive/collaborative processes. I will also present the analysis of epidemic spreading processes with infectious but asymptomatic states and of interlinked spreading processes on different networks, and the effectiveness of containment and mitigation in both cases.
