>> DIEP seminars and talks
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.
>> DIEP Seminar: Ricard Solé (ICREA-Complex Systems Lab)
Emergence, tinkering and universality in evolved networks | 11am, 15th of December 2022
A common trait of complex systems is that they can be represented using a network of interacting parts. In fact, the network organization (more than the parts) largely conditions most higher-level properties, which are not reducible to the properties of the individual parts. Can the topological organization of these webs provide some insight into their evolutionary origins? Both biological and artificial networks share some common architectural traits. They are often heterogeneous and sparse, and most exhibit different types of correlations, such as nestedness, modularity or hierarchical patterns. These properties have often been attributed to the selection of functionally meaningful traits. However, a proper formulation of generative network models suggests a somewhat different picture. Against the standard selection–optimization argument, some networks reveal the inevitable generation of complex patterns resulting from reuse and can be modelled using duplication–rewiring rules lacking functionality. In other examples, such as human language, information tradeoffs might be responsible for the presence of universal scaling laws. Both give rise to the observed heterogeneous, scale-free and modular architectures. Here, we examine the evidence for tinkering and universality in cellular, technological and ecological webs and its impact on shaping their architecture. We suggest that both tinkering and information constraints shape these graphs at the topological level. In biological systems, selection forces would take advantage of emergent patterns.
>> DIEP Seminar: Koen van der Zwet (U. Amsterdam)
Opportunistic organisation of illicit supply chains | 11am, 8th of December 2022
Opportunistic and small criminal groups are the predominant form in organised criminal markets. As a collective the opportunistic groups form an extensive network of contacts that enables organisation of more sophisticated processes. In the past decade, the network modelling approach has become the novel and focal data-driven method to analyse these criminal organisations. The developed models aim to identify key persons and vulnerabilities of criminal organisations. However, network studies are often limited to analysing static representations. As a consequence, the adaptive capacity of criminal networks remains poorly understood.
In this talk I present an agent-based model that incorporates the necessary dimensions to represent a generalisation of the dependencies in illicit supply chains, and model different organisation dynamics that enable individuals to share information and subsequently make strategic decisions. Two network approaches are applied to observe the core concepts of interaction, adaptation, and emergence in the illicit supply chain system. First, a hypergraph approach is used to analyse the opportunistic group interactions. Second, a multilayer network approach is introduced to model dependencies of the illicit activities, enduring social relationships, and fluid transaction relationships. This simulation-based approach enables us to analyse the effectiveness of the emergent transactions generated by the interplay of organisational structure and dynamics.
>> DIEP Seminar: Astrid Groot (U. Amsterdam)
How to measure evolution? | 11am, 24th of November 2022
Evolution is the change through time, where time is measured over generations. Evolutionary histories can be deduced by comparing phylogenetic species trees to genetic changes in genes that have been identified to encode phenotypic traits, such as eye color or odor perception. However, whether and to what extent evolutionary predictions can be made with genetic information is elusive, because a) selection acts on the phenotype, which is determined by genes as well as the environment, and b) different genetic pathways can lead to the same phenotype. Our long-term research on the evolution of chemical communication in relation to speciation in moths (Lepidoptera, Noctuidae) illustrates the difficulty to translate evolution at the gene level to evolution at the organism (phenotype) level. Olfactory receptors determine which odors can be detected by which animals. Since moths find their mates through sex pheromones, the evolution of moth species is hypothesized to be reflected in the evolution of olfactory receptors. However, in our experimental analysis with a moth species that consists of two pheromone strains, we found that not olfactory receptors but a gene involved in neuronal development underlies the difference in response. Even though this result has led to new directions in our experimental work, it poses questions on whether and how information at the genetic level can be used to predict evolution. In our NWA Origins of Life project Predicting evolution, we developed a testable method to predict evolution at the genetic level, using the model organism Caenorhabditis elegans (a nematode, see http://www.wormbook.org/).
>> DIEP Seminar: Eddie Lee (Complexity Science Hub Viena)
Idea engines: Unifying innovation and obsolescence from markets and genetic evolution to science | 11am, 1st of December 2022
Innovation and obsolescence describe dynamics of ever-churning and adapting social and biological systems, concepts that encompass field-specific formulations. We formalize the connection with a toy model of the dynamics of the „space of the possible“ (e.g. technologies, mutations, theories) to which agents (e.g. firms, organisms, scientists) couple as they grow, die, and replicate. We predict three regimes: the space is finite, ever growing, or a Schumpeterian dystopia in which obsolescence drives the system to collapse. We reveal a critical boundary at which the space of the possible fluctuates dramatically in size, displaying recurrent periods of minimal and of veritable diversity. When the space is finite, corresponding to physically realizable systems, we find surprising structure. This structure predicts a taxonomy for the density of agents near and away from the innovative frontier that we compare with distributions of firm productivity, covid diversity, and citation rates for scientific publications. Remarkably, our minimal model derived from first principles aligns with empirical examples, implying a follow-the-leader dynamic in firm cost efficiency and biological evolution, whereas scientific progress reflects consensus that waits on old ideas to go obsolete. Our theory introduces a fresh and empirically testable framework for unifying innovation and obsolescence across fields.
>> DIEP Seminar: Bernd Ensing (U. Amsterdam)
Understanding molecular transitions as pathways in free energy landscapes | 11am, 17th of November 2022
After laying out his momentous contribution to the foundation of quantum mechanics, Paul Dirac allegedly said: “… and the rest is chemistry!”. Apparently, Dirac had no idea of the richness and complexity of observable phenomena that emerge as solutions from his Dirac (or Schrödinger’s) equation. In this talk, I will showcase several material properties in soft matter and aqueous systems that are, at first sight, remarkably surprising when just regarding the microscopic chemical constituents. Molecular dynamics simulations can be very helpful to unravel the molecular interactions and mechanisms that underly these observable material properties. Molecular transitions, such as chemical reactions, phase transitions, and self-assembly processes can be comprehensibly represented as pathways on low-dimensional free energy landscapes, but this entails finding the essential reaction coordinate(s) or the few collective variables in the many-particle system that describe the molecular process, which is often far from trivial. During this talk, I will present several in-house developed enhanced sampling methods that we apply for this purpose, including a reinforcement learning approach that does not require prior knowledge of the collective variables.
>> DIEP Seminar: Sara Walker (U. Arizona)
When Time is an Object (and Implications for Emergence) | 4pm, 10th of November 2022
Most of the history of physics has been one of removing time from our fundamental descriptions of nature. Newton did this by writing laws of physics that are timeless and exist outside of the universe. Einstein’s universe carries an implication that we live in a universe where time does not pass, and our perceptions of its passage are therefore illusory. In the world of Boltzmann and other statistical mechanists time is an emergent property arising due to the second law. We have not yet had a theory of physics (successful in the long term) where time is fundamental. The goal of this talk is to provoke discussion on why this may the root cause of issues with understanding and quantifying emergence as a physical property, with specific focus on the ‘emergence of life’. In chemistry and biology, we find objects that require memory to produce them because they are too complex to be produced by random chance. This insight forms the foundation of a new theory, called assembly theory, which describes how much memory must exist for a molecular object to come into existence, with the implication that more evolved something is, the more memory is required. Using molecular assembly, we have demonstrated how it is possible to agnostically (in absence of specific details) identify complex molecules as signatures of life empirically using Mass Spec. One of the most interesting implications – if the theory holds – is what it tells us about how time exists in complex objects created by evolution, like the ones we find all around us in the biosphere and technosphere. Objects created by evolutionary processes require time for their formation, because they exist across time – time is a physical attribute. The talk will explore the consequences of re-envisioning time as an observable property of complex matter, including for the general feature of ‘emergence’ being associated to objects that are deep in time.
>> DIEP Seminar: Fernando P. Santos (U. Amsterdam)
Reputation dynamics and the emergence of human cooperation | 11am, 3rd of November 2022
Indirect Reciprocity – Alice behaves adequately towards Bob; Carol knows about it and thus helps Alice – is a central mechanism to explain the emergence of cooperation between humans. Simultaneously, indirect reciprocity can inspire new ways of engineering cooperation in multiagent (artificial) systems where reputations are paramount. A central challenge in indirect reciprocity is understanding which social norms – here rules defining how reputations should be attributed – lead to the highest levels of cooperation. I will address this challenge through evolutionary game theoretical models that formalize the coupled dynamics of reputations and cooperation as a stochastic process. I will then discuss ways of quantifying the complexity of norms and strategies and ask which social norms promote maximal cooperation at minimal complexity. Finally, I will discuss models of indirect reciprocity on complex networks and discuss the emergence of reputation-driven polarization in these settings.
>> DIEP Seminar: Fleur Zeldenrust (Radboud University Nijmegen)
Emergent properties of networks in the brain: the relation between structure and computation | 11am, 27th of October 2022
The brain continuously processes information. The electrical activity of the neural networks that make up the brain is both irregular and unreliable. How is it possible that the brain can perform stable computations on the basis of such chaotic activity? In this presentation, I will discuss how such chaotic activity emerges, how it is influenced by the nonlinear properties of the network nodes, and different theories on how the brain can perform stable computations on the basis of such unreliable activity.
>> DIEP Seminar: Esmee Geerken (IAS resident artist)
The Holebearers | 11am, 13th of October 2022
In her talk, Esmee will introduce some of her current Art-Science projects, touching upon emergence, self-organization, forms and phase-shifts in ‘systems’ at various spatial scales. From human cells interacting with growing crystals to unicellular organisms precipitating their shells from oceanwater to end up in the rocks we use for our building materials, from human-scale architectures (e.g. Amsterdam Science Park) to the ‘shelters’ we build within our human mind, Esmee is interested in how shapes and forms emerge, and in the role and agency of individuals (or individuality) within these larger ‘complex systems’, and in navigating our understanding of the world through storytelling.
Furthermore, Esmee hopes to engage in a dialogue with the DIEP community, to learn more about inherent chance, entropy and information, and is curious to learn more on how creative, scientific insights emerge in your minds, for an upcoming project.
>> DIEP Seminar: Jocelyne Vreede (U. Amsterdam)
From atomic interactions to cellular processes | 11am, 29th of September 2022
In this seminar, I will explore how interactions between atoms can provide understanding of cellular processes, using molecular simulation techniques. Signal transduction is a mostly intracellular process that starts with a small trigger and ends with a response on a much larger scale. For example, the detection of a few photons can lead to a response involving the head turning towards the flash of light. Triggers initiating a signal transduction pathway can be many things, including a change in the concentration of a chemical compound. The subsequent response can vary widely, with altered gene expression, triggering an action potential and directional growth as examples. Understanding how a small trigger leads to a cell-wide response requires insights at atomic resolution in both space and time. Models based on atomic interaction potentials in combination with equations of motion can predict macroscopic properties of molecular systems. With molecular dynamics simulations it is possible to indicate, for instance, the various ways in which a protein can bind to DNA. If simulated long enough, such predictions can be quantitative, such as the affinity of a protein for a particular DNA sequence. A problem in molecular simulation of biomolecular systems that simulating long enough is almost always impossible, as the timescale of the event of interest usually is many orders of magnitude larger than the atomic fluctuations at which scale sampling takes place. Focusing on relevant collective coordinates provides a way to overcome this sampling problem and allows for fast predictions.
Using two examples I will illustrate how molecular simulation can aid in the understanding of cellular processes. The first example focuses on elucidating the molecular mechanisms with which plants can detect sodium. Crop loss due to soil salinisation is an increasing threat to agriculture world wide, as plants have an adverse response to sodium. To date, it is unknown how plants sense sodium. Using a molecular simulation protocol, we are making progress in unraveling this signal transduction process. The second example involves the regulation of gene expression. Whether a gene is expressed depends on many factors, including the molecular configuration and shape of the DNA and the presence of regulating proteins. With molecular simulation it is now possible to quantitatively predict the affinity of a protein for a specific nucleotide sequence, opening up new ways of studying gene expression and other processes involving interactions between proteins and DNA.
>> DIEP Seminar: Enrico Cinti (U. Urbino and U. Geneva)
Spacetime emergence inside black holes | 11am, 15th of September 2022
The problem of the disappearance of spacetime has long been recognized as one of the most pressing philosophical and conceptual problems facing theories of quantum gravity. In this talk, my goal will be to look at this issue from the point of view of AdS/CFT, focusing in particular on the relationship between the non-perturbative definition of quantum gravity given by the duality and the semiclassical description of gravity given by the effective field theory in the bulk. By thinking in particular about the interior of black holes and the reconstruction map connecting their effective description to their fundamental one, I come to the surprising conclusion that the standard answer to the problem of the disappearance of spacetime, i.e. emergence, is inadequate in this case, at least as standardly formulated. Rather, I suggest that a more flexible and less ontologically demanding approach is required, whose basic features I outline.
>> DIEP Seminar: Artemy Kolchinski (U. Tokyo)
Information geometry of fluxes and forces in nonequilibrium thermodynamics | 11am, 8th of September 2022
A nonequilibrium system is characterized by a set of thermodynamic forces and fluxes which give rise to entropy production (EP). We show that these forces and fluxes have an information-geometric structure, which allows us to decompose EP into nonnegative contributions from different types of forces. We focus in particular on the excess and housekeeping decomposition, which identifies contributions from conservative and nonconservative forces. Unlike the well-known nonadiabatic/adiabatic (Hatano-Sasa) approach, our decomposition is always well-defined, including in systems with odd variables and nonlinear systems without steady states. It is also operationally meaningful, leading to far-from-equilibrium thermodynamic uncertainty relations and speed limits. (joint work with Andreas Dechant, Kohei Yoshimura, and Sosuke Ito / https://arxiv.org/abs/2206.14599)
>> DIEP Seminar: Sid Redner (Santa Fe Institute)
The Dynamics of Diversity and Polarization | 11am, 30th of June 2022
If opinions spread by interactions between reasonable individuals, why is consensus so unlikely? I present some idealized social dynamics models, which are based on social interactions that decay with distance in opinion space, to understand why diversity is common. I'll first describe the Axelrod model, in which multi-feature individuals move closer in opinion space only if they agree on some other feature. Another example is a 3-state voter model of leftists, centrists, and rightists, in which a centrist and an extremist influence each other but extremists of the opposite persuasion do not. Depending on the initial condition, the population may or may not reach consensus. Similarly, in the bounded compromise model, a population becomes increasingly fragmented as the political range of interaction of an individual decreases. A related polarization phenomenon arises in the voter model when individuals are also influenced by competing and fixed external news sources.
>> DIEP Seminar: Tiziano Squartini (IMT Luca)
From graphs randomization to hypergraphs randomization | 11am, 23rd of June 2022
Network theory has emerged as a powerful paradigm to explain phenomena where units interact in a highly non-trivial way. So far, however, research in the field has mainly focused on pairwise interactions, disregarding the possibility that more-than-two constituent units could interact at a time. Hypergraphs represent a class of mathematical objects that could serve the scope of describing this novel kind of many-bodies interactions. In this paper, we propose benchmark models for hypergraphs analysis that generalise the usual Erdos-Renyi and Configuration Model in the simplest possible way, i.e. by randomising the hypergraph incidence matrix while preserving the corresponding connectivity/topological constraints - whose definition is, now, adapted to the novel framework. After exploring the mathematical properties of the proposed benchmark models, we consider two different applications: first, we define a novel quantity, the hyperedge assortativity, whose expected value we theoretically derive for all the introduced null models and which we, then, use to detect deviations in the corresponding real-world hypergraphs; second, we define a principled procedure for testing the statistical significance of the number of hyperedges connecting any two nodes.
>> DIEP Seminar: Pim van der Hoorn (TU Eindhoven)
Geometry and complex networks. A powerful partnership | 11am, 16th of June 2022
Geometry is a powerful tool in many scientific domains, from fundamental physics to social science. It also plays and important role in the study of complex, especially from the point of constructing models for networks. Here nodes represent positions in some geometric space and connections are created based on the distances in that space. In this talk I will highlight results for two key aspects of this use of geometry. In the first part I will discuss a model for complex networks that uses Hyperbolic geometry. This geometry turns out to naturally lead to networks that exhibit key features found in many real-world networks: sparse, power-law degrees and clustering. Here I shall present new results on the clustering of this network model. In the second part I shall touch upon the challenge of recovering geometric information from networks. That is, if we see the resulting network can we identify the underlying geometric space? I will discuss a discrete version of curvature for networks developed by Yann Ollivier. Then I will present results that show that this notion can actually uncover the curvature of hidden geometry and large scale simulation that show how well this works for Euclidean, Spherical and Hyperbolic spaces.
>> DIEP Seminar: Muhammed Sayin (Bilkent University)
Towards the Foundation of Dynamic Multi-agent Learning | 11am, 9th of June 2022
Many of the forefront applications of reinforcement learning involve multiple agents and dynamic environments, e.g., playing chess and Go games, autonomous driving, and robotics. Unfortunately, there has been limited progress on the foundation of dynamic multi-agent learning, especially independent learning in which intelligent agents take actions consistent with their individual objectives, e.g., based on behavioral learning models, quite contrary to the plethora of results on algorithmic solutions.
In this talk, I will present a new framework and several new independent learning dynamics. These dynamics converge almost surely to an equilibrium or converge selectively to an equilibrium maximizing the social welfare in certain but important classes of Markov games -- ideal models for decentralized multi-agent reinforcement learning. These results can also be generalized to the cases where agents do not know the model of the environment, do not observe opponent actions, and can adopt different learning rates. I will conclude my talk with several remarks on possible future research directions for the framework presented.
>> DIEP Seminar: Sabin Roman (U. Cambridge)
Modelling the long-term evolution and collapse of societies | 11am, 2nd of June 2022
The talk will cover some key modelling issues that come up when considering the long-term development of societies. Of particular importance is the topic of societal collapse as the archaeological record has numerous instances of the phenomenon. Sabin will discuss some of the general modelling philosophy, relevant literature, his own work on ancient societies (Easter Island, the Maya, Roman Empire and Chinese dynasties) and implications for modern society. There are several modelling considerations unique to modern society that will be highlighted.
>> DIEP Seminar: Manilo de Domenico (University of Padua)
Emergent phenomena in human networks and dynamics: infodemics and epidemics | 11am, 19th of May 2022
Complex systems are characterized by constituents -- from neurons in the brain to individuals in a social network -- which often exhibit a special structural organization and nonlinear dynamics. As a consequence, a complex system can not be understood by studying its units separately because their interactions lead to unexpected emerging phenomena, from collective behavior to phase transitions. In the last decade, we have discovered that a new level of complexity characterizes a variety of natural and artificial systems, where units interact, simultaneously, in distinct ways. For instance, this is the case of multimodal transportation systems (e.g., metro, bus and train networks) or of social networks, whose interactions might be of different type (e.g. trust, trade, virtual, etc.). The unprecedented newfound wealth of data allows to categorize system's interdependency by defining distinct "layers", each one encoding a different network representation of the system. The result is a multilayer network model. In this talk we will discuss the most salient features of multilayer systems, with special attention to empirical human communication/mobility networks responsible for emergent phenomena such as infodemics and epidemics.
>> DIEP Seminar: Greg Stephens (VU Amsterdam)
Bridging timescales in partially observed dynamical systems | 11am, 12th of May 2022
In approaches such as Langevin, combining fast fluctuations with slower dynamics has proven remarkably powerful. But how do we proceed in generally out-of-equilibrium systems for which we lack underlying equations? Here we construct short-time, maximally-predictive states by concatenating measurements in time, partitioning the resulting sequences using maximum entropy, and choosing the sequence length to maximize short-time predictive information. We use transitions between these states to analyze reconstructed dynamics through transfer operators, revealing timescale separation with long-lived collective modes through the operator spectrum. Applicable to both deterministic and stochastic systems, we illustrate our approach through partial observations of the Lorenz system and the stochastic dynamics of a particle in a double-well potential. Applied to the behavior of the nematode worm C. elegans, we bridge sub-second posture fluctuations and long range effective diffusion in foraging behavior, recovering the ``ballistic-to-diffusive'' transition in the worm's centroid trajectories. We use transfer operators to reveal long-lived ``run-and-pirouette'' behaviors, and predict additional subtle subdivisions of the worm's ``run'' dynamics.
>> DIEP Seminar: Katarzyna Sznajd-Weron (Wroclaw University)
Private Truths, Public Lies” within agent-based modeling | 11am, 28th of April 2022
The title of this talk was inspired by Timur Kuran's book entitled "Private Truths, Public Lies. The Social Consequences of Preference Falsification". During the presentation I will discuss the idea of Preference Falsification (PF) and propose a simple binary agent-based model to describe PF by introducing two levels of opinion: public and private. I will show how the second (hidden) level of opinion affects the phase transitions occurring in the system, and thus how it can help to understand the origin of the so-called social hysteresis. This research is a part of the project Towards understanding of the social hysteresis: an agent-based approach, NCN 2019/35/B/HS6/02530.
>> DIEP Seminar: Hendrik Baier (TU Eindhoven)
A Vision for Explainable Search, and Some First Steps Towards It| 11am, 21st of April 2022
In his presentation, Hendrik is going to sketch one of the research themes he is developing - explainable search. The first part of this talk is going to define and motivate the problem: While AI agents based on search or online planning are state of the art in many challenging domains, famously in board games, current approaches lack the ability to explain, summarize, or visualize their plans and decisions. Users struggle to understand how observable behavior is derived from considering complex spaces of possible futures, contingencies, and eventualities, spanned by the available actions of the agent. This limits human trust in high-stakes scenarios, as well as effective human-AI collaboration. Hendrik will outline the proposed research direction of explainable search, and important related research challenges.
The second part of the talk is then going to move from challenges to concrete first steps towards solving them. Focusing on algorithms in the Monte Carlo Tree Search (MCTS) family, at the heart of many recent breakthroughs in AI, Hendrik is going to present his ongoing exploration of possible explanations for sequential decision-making and behavior. This work is tackling for the first time some of the challenges previously posed for explainable search, such as: meaningfully summarizing the space of possible futures spanned by the available actions of the AI and their possible consequences, in order to explain how the AI’s choices between them emerge; considering such explanations not only as static objects but as interactive conversations between user and AI; and understanding explanation not only as a one-way information flow from the AI to the user, but as a tool for human-AI collaboration and for leveraging both AI and human capabilities in problem solving.
>> DIEP Seminar: Chris Reinders Folmer (U. Amsterdam)
Beyond narrow perspectives on prosocial behavior: benefits of aggregation of game behavior | 11am, 14th of April 2022
When using economic games to study prosocial, cooperative behavior, research tends to zoom in on highly specific settings: a specific type of game, represented with a specific set of parameters, and a specific, narrow subset of treatments. However, some research suggests that behavior in such narrow settings is only modestly related to other indices of prosociality, such as prosocial personality traits and mundane prosocial behaviors. In this research, we advocate a broader approach that moves beyond such narrow perspectives, by aggregating choice behavior across different representations and types of games. We find that aggregation across different settings strengthens associations with prosocial personality, and with prosocial behaviors in mundane settings. Based on this, we recommend for research to move beyond narrow approaches to prosocial behavior, to broad approaches based on aggregation.
>> DIEP Seminar: Maurice Weiler (U. Amsterdam)
Coordinate independent convolutional networks - Isometry and gauge equivariant convolutions on Riemannian manifolds | 11am, 7th of April 2022
Motivated by the vast success of deep convolutional networks, there is a great interest in generalizing convolutions to non-Euclidean manifolds. A major complication in comparison to flat spaces is that it is unclear in which alignment a convolution kernel should be applied on a manifold. The underlying reason for this ambiguity is that general manifolds do not come with a canonical choice of reference frames (gauge). Kernels and features therefore have to be expressed relative to arbitrary coordinates. We argue that the particular choice of coordinatization should not affect a network's inference - it should be coordinate independent. A simultaneous demand for coordinate independence and weight sharing is shown to result in a requirement on the network to be equivariant under local gauge transformations (changes of local reference frames). The ambiguity of reference frames depends thereby on the G-structure of the manifold, such that the necessary level of gauge equivariance is prescribed by the corresponding structure group G. Coordinate independent convolutions are proven to be equivariant w.r.t. those isometries that are symmetries of the G-structure. The resulting theory is formulated in a coordinate free fashion in terms of fiber bundles. To exemplify the design of coordinate independent convolutions, we implement a convolutional network on the Möbius strip. The generality of our differential geometric formulation of convolutional networks is demonstrated by an extensive literature review which explains a large number of EuclideanCNNs, spherical CNNs and CNNs on general surfaces as specific instances of coordinate independent convolutions.
>> DIEP Seminar: Jaap Kaandorp (U. Amsterdam)
Modelling embryogenesis and biomineralization in sea anemones, jelly fish and corals | 11am, 24th of March 2022
Rather than being directed by a central control mechanism, embryogenesis can be viewed as an emergent behavior resulting from a complex system in which several sub-processes on very different temporal and spatial scales (ranging from nanometer and nanoseconds to cm and days) are connected into a multi-scale system. In our research we have been focusing on the embryogenesis of basal organisms like the non-calcifying sea anemone Nematostella vectensis and the hydrozoan Clytia hemisphaerica and the calcifying coral Acropora millepora. We have developed methods for analysing spatio-temporal gene expression patterns, methods for spatio-temporal modelling and inferring gene regulatory networks from gene expression data (qPCR data and in–situ hybridizations) and a cell-based mechanical model of early embryogenesis. In the coral we have developed a model of calcification physiology controlling biomineralization. Currently we are investigating how the different levels of organization (gene regulation controlling embryogenesis, cell mechanics and biomineralization) can be coupled into a multi-scale model of embryogenesis.
>> DIEP Seminar: Fernando N. Santos (Amsterdam UMC)
Emergence of High Order Hubs in the Human Connectome | 11am, 17th of March 2022
In this talk, I would like to share early results on our ongoing work with colleagues at IAS and VUmc Amsterdam on high-order interactions in functional brain networks. Network theory is predominantly based on pairwise relationships between nodes, which is not realistic for most complex systems. In particular, it does not accurately capture nondyadic interactions in the brain. Over the past years, I have been interested in inferring high-order interactions from brain signals and exploring their consequences for our understanding of the human brain as a high-order network.
The talk will be divided in two parts: i) methodology and ii) application. I will first introduce the methodology we developed to analyze the brain as a high-order network, in particular the multivariate signal processing pipeline that can define high-order interactions and connectivity in rs-fMRI images of brain networks (or any other time series signal) in an intuitive way. I will then present preliminary result from our current applied work, in which we searched for high-order "hubs" in a cohort of 100 individuals from the Human Connectome project as a proof of concept. In fact, even though we did not consider any prior knowledge about the functionality of specific brain regions in our analysis, we found that well-known integration and segregation patterns in the brain emerge spontaneously from the high-order hubs and can be considered emergent properties of functional brain networks. In this context, each type of high-order interdependency is compatible with distinct systems in the brain. For instance, three-point interactions seem to manifest segregation and integration principles consistent with the sensory-motor and visual systems. We believe that this opens up exciting avenues for further research.
>> DIEP Seminar: Mark Golden (U. Amsterdam)
How the photoelectric effect on rusty copper can test AdS-CFT in real life | 10th of February 2022
When the (many) electrons in crystals interact strongly with each other the system behaves in ways that condensed matter theory often cannot predict, and even to try to brings enormous computational cost. The discovery of high temperature superconductivity in a copper oxide (hence the rust) is a poster-child for such unexpected behaviour, and ‘emergent’ has been used for decades to describe the behaviour of such strongly correlated electron systems.
Usually a superconductor is born out of a metallic quantum state called a Fermi liquid, on lowering the temperature. In the rusty-copper high temperature superconductors the parent high temperature state is not a normal metal. As it breaks many electron behavioural rules and we don’t (yet) understand it, the name it gets is a ‘strange metal’.
In this seminar I’ll try to explain how a simple experiment (photoelectric effect, see pic) can spy on the mysterious inner workings of quantum materials such as the strange metals. In the case of our crystal of rusty copper, in which the electrons live essentially in 2 spatial dimensions (and time), the data show a behaviour so strange that we allow ourselves to look further than condensed matter theory for help. The cavalry on the hillside riding to our rescue take the form of gravitational problems connected to black holes in 3+1 dimensions, soluble using the tools of string theory.
So, perhaps simple lab experiments taking place here in the SciencePark seem to be able to connect with and test the correspondence between general relativity in anti-de-Sitter space and conformal field theory (AdS-CFT). It seems the emergent geometries of AdS-CFT seem to help explain the strange metal behaviour of emergent high temperature superconductors.
Though many have been involved in this research I’d like to highlight the PhD researchers Steef Smit (lead experimentalist; UvA) and Enea Mauri (with Henk Stoof lead theorists @UU).
For the first paper out on this, see: Smit et al., arxiv.org/pdf/2112.06576
>> DIEP Seminar: Peter Bolhuis (U. Amsterdam)
Understanding emergent rare event behavior in high dimensional systems | 3rd of February 2022
Microscopic dynamics governs the macroscopic behavior of complex molecular systems, from materials to living cells. To predict this emerging behavior, physics-based atomistic models combined with well-established equations of motion provide a high dimensional dynamical system, which, given an initial condition, can be time-integrated to yield the long-time behavior for the system, and hence its macroscopic properties. In principle, one could use accurate quantum mechanics for this time evolution, but in all but a few cases this is utterly unfeasible and intractable. Fortunately, coarse-graining in time and/or space, neglecting or integrating out degrees of freedom leads to simpler descriptions, which make the problem tractable. This leads to molecular dynamics (MD) based on effective (empirical) force fields, which can be designed to reproduce experimental or (quantum mechanically) computed observables. However, even in such empirical MD simulations timescales can be quite long, caused by the presence of dynamical bottlenecks between metastable states. Exploring these transitions by direct MD is extremely costly, for example in the case of protein folding or nucleation. Further coarse graining towards simpler force fields could solve this problem, but then important molecular details might get lost. A better option is to coarse-grain in time and make use of the emergent Markovianity of the dynamics, which leads to a probabilistic Markov state description. Still, this requires knowledge of the state-to-state transition rates that are hard to obtain by direct MD as these transitions occur very rarely on the timescale of the simulation. This rare event problem can be solved by computing the probability to find the system at the dynamical bottleneck, the transition state, by slowly restraining the system towards it. This in turn requires a way to identify this transition state by a collective variable or reaction coordinate (sometimes also called the importance function). This very important reaction coordinate (RC) is ideally a low dimensional representation of the high-dimensional space. Finding it requires a dimensionality reduction of the phase space, but is often elusive. In fact, it is kind of a chicken and egg problem: To study the rare event, one needs the RC, but to find the RC one needs to observe the rare event first.
A solution to this problem is to use the concept of trajectory sampling. One can sample trajectories between easily identifiable metastable states, and extract the RC from the resulting path ensemble by a dimensionality reduction, e.g. using machine learning. The thus obtained knowledge can then be used to better describe the kinetics and dynamics in complex molecular systems.
Of course, this approach still relies on accurate force fields. Recent work demonstrated that it is possible to correct for force field inaccuracies by incorporating experimental kinetic constraints into the computed trajectory ensemble, through the use of the maximum (path) entropy principle, even further improving our understanding of rare events in complex systems.
>> DIEP Seminar: Tomas Veloz (Vrije Universiteit Brussel)
Chemical organisation theory, structural changes, and emergence | 27th January 2022
Chemical Organization Theory (COT) is an approach to compute closed and self-maintaining reaction networks (RNs) called organizations. Organizations represent an abstraction of the attractors of the dynamics of the RN. In this sense, they reflect the stable-enough-to-be-observed parts of the RN and thus might serve as a model of emergent structures. Organizations form a partial ordered set (POS), which in many cases can be equipped with operators to become a lattice. This connection between RN and mathematical logic opens interesting and new issues. One of them is that RNs can be perturbed by adding or subtracting reactions (meaning externally or evolutionarily induced structural changes), and such changes modify the lattice properties. I will present a decomposition theorem for organizations which allows to study in more detail the impact of a structural perturbations on the organizations lattice, and the potential changes at a logical level that can occur.
>> DIEP Seminar: André de Roos (U. Amsterdam)
Everything is a network, but the network is not everything: Dynamics of stage-structured food webs | 20th of January 2022
Ecological communities are traditionally viewed as networks of negative and positive interactions between species, while the species themselves are seen as collections of identical individuals. Using this conceptualisation, however, it had been difficult to explain the importance of biodiversity. In fact, analysis of the topology of ecological interaction networks has shown that “diversity begets instability”: more complex and more diverse communities tend to be dynamically unstable and hence not persist. Complex communities are predicted to be stable only when species growth rates are mostly limited by intraspecific self-regulation (within-node limitation) rather than by interactions with resources, competitors, and predators.
In this talk I show how adding a second axis of complexity to the study of ecological interaction networks yields contrasting predictions about the relation between community diversity and stability. This second axis recognises that conspecific individuals are different from one another, first and foremost, because they are in different stages of development. Using food web models that account for juvenile and adult individuals of species, I show that commonly observed differences between juveniles and adults in foraging capacity and predation risk result in larger, more complex communities than predicted by models without stage structure. Based on their species interaction networks these complex and diverse communities would be expected to be unstable, but these destabilizing effects of species interactions are overruled by stabilizing changes in juvenile–adult stage structure. Differences between juvenile and adult individuals hence offer a natural resolution to the diversity–stability enigma of ecological communities.
>> DIEP Seminar: Bjarke F. Nielsen (U. Roskilde)
Heterogeneous disease transmission - superspreading and the case for statistical physics | 9th of December 2021
The transmission pattern of SARS-CoV-2 has proven to be very heterogeneous, with a tendency towards superspreading. So much so that it has been estimated that just 10% of infected individuals give rise to 80% of new cases. This finding is surprisingly robust and has been reproduced by several methods, including studies based on contact tracing, aggregated incidence data and phylodynamics. Recent mathematical models have shown that this feature has profound - and sometimes positive - implications for the effectiveness of some non pharmaceutical interventions. Capturing the phenomenon requires concepts and methods from statistical physics, such as agent-based modelling and the distinction between quenched and annealed noise, and would not be possible in traditional, compartmental homogeneous-mixing models of disease spread. In a broad sense, these results highlight the importance of taking heterogeneity into account and provide a strong argument for the introduction of novel, statistical physics-inspired models into infectious disease modelling. Lastly, I will comment on how transmission heterogeneity and interventions can interact with pathogen evolution and the emergence of new variants.
>> DIEP Seminar: Tomas Veloz (Vrije Universiteit Brussel)
Reaction Networks and Evolutionary Game Theory | 2nd of December 2021
Mathematical approaches in systems biology are increasingly applied beyond the scope of biology. Particularly, reaction networks have been suggested as an alternative way to model systems of a general kind, and particularly social phenomena. In this latter “socio-chemical metaphor” molecular species play the role of agents’ decisions and their outcomes, and chemical reactions play the role of interactions among these decisions. From here, it is possible to study the dynamical properties of social systems using standard tools of biochemical modelling. In this talk we show how reaction networks can model systems that are usually studied via evolutionary game theory.
We illustrate our framework by modeling the repeated prisoners’ dilemma. We further develop a model considering the interaction among Tit for Tat and Defector agents.
We will discuss the strengths and weaknesses of the approach as well as its potential to produce new insights in classical problems such as the emergence of goal-directedness and the evolution of cooperation.
>> DIEP Seminar: Physics Nobel Prize 2021
An invitation to the work of Hasselmann, Manabe and Parisi | 25th of November 2021
The Nobel Prize in Physics 2021 was awarded "for groundbreaking contributions to our understanding of complex systems" with one half jointly to Syukuro Manabe and Klaus Hasselmann "for the physical modelling of Earth's climate, quantifying variability and reliably predicting global warming" and the other half to Giorgio Parisi "for the discovery of the interplay of disorder and fluctuations in physical systems from atomic to planetary scales."
The Dutch Institute for Emergent Phenomena invites you to attend a series of short lectures by John Mydosh, Edan Lerner, Elisabetta Pallante, Luca Giomi and Daan Crommelin on aspects of the Nobel Prize 2021 that cover many of the contributions that Hasselmann, Manabe and Parisi made to spin glasses, particle physics, flocking and climate models. The event takes place online. To receive the Zoom link before the event register below.
>> DIEP Seminar: Han van der Maas (U. Amsterdam)
Cascading transition in psychology | 18th of November 2021
Tipping points or phase transitions separate stable states in psycho-social systems. Examples are quitting smoking, radicalization, and dropping-out of school. Two knowledge gaps prevent our ability to predict and control these tipping points. First, we miss explanatory mathematical models of such non-linear processes. Second, we ignore the multilevel character of psycho-social transitions. I contend that important changes in many psycho-social systems are cascading transitions, where individual transitions trigger or are triggered by social transitions. The cascade of radicalization of individuals in the context of political polarization in societies is an example of such a multilevel process. Being able to predict and control cascading transitions in psycho-social systems would be a major scientific breakthrough.
As an expert of complex systems research in the behavioural and social sciences, with an extensive track record in studying single level psychological transition processes (e.g., in perception, sleep, disorders, cognition, and attitudes), my key objective is to develop a novel and broadly applicable methodology to study multilevel cascading transitions in psycho-social systems. This methodology comprises theory construction in the form of mathematical modelling and innovative empirical analyses. I will do so by studying three important examples: a) opinion change from individuals to populations and back, b) learning, where progression and drop-out are embedded in collective processes, c) addiction, where transitions to addiction or abstinence within individuals are part of cascading epidemiological changes of substance use in populations.
>> DIEP Seminar: Mari Carmen Banuls (Max Planck Institute of Quantum Optics)
Tensor networks for classical systems | 11th of November 2021
Tensor network states (TNS) are a very useful tool for the study of correlated many-body systems. In the context of quantum many-body problems, they provide precise and efficient methods to explore thermal equilibrium and ground states. And it is also possible to use them, to some extent, for the study of out of equilibrium dynamics.
TNS can also be applied to classical problems. One the one hand, it is possible to compute partition functions and observables in equilibrium. Furthermore, they can be combined with Montecarlo techniques to sample from a Boltzmann distribution for a wide variety of models. On the other hand, also dynamical scenarios can be studied with these techniques. In particular, we have recently shown how they can be used for studying large deviation functions and sampling rare trajectories in constrained stochastic models.
>> DIEP Seminar: Hugo Touchette (Stellenbosch University)
The large deviation approach to statistical physics | 4th of November 2021
I will give in this talk a basic overview of the theory of large deviations and of its applications in statistical physics. In the first part, I will discuss the basics of this theory and its historical sources, which can be traced back in mathematics to Cramer (1938), Sanov (1960) and Varadhan (1970s) and, on the physics side, to Einstein (1910) and Boltzmann (1877). In the second part, I will discuss how the theory has been applied in recent years to study various equilibrium, nonequilibrium, and complex systems, such as interacting particle systems, turbulent flows, and random graphs, among many examples.
>> DIEP Seminar: Wolfram Barfuss (U. Tübingen)
Collective Learning Dynamics | 28th of October 2021
Collective learning will become of vital importance not only in genuine multi-agent systems, such as mobility, swarm robotics, or public infrastructure, it is also crucial to consider when applied machine learning systems are changing the very same data they have been trained upon. The question of an adequate theoretical foundation for multi-agent learning, however, remains unanswered. I will show how techniques from evolutionary game theory and statistical mechanics can provide an improved understanding of the emerging collective learning dynamics in changing environments.
>> DIEP Seminar: Matthijs van Veelen (U. Amsterdam)
The evolution of morality and the role of commitment | 21st of October 2021
A considerable share of the literature on the evolution of human cooperation considers the question why we have not evolved to play the Nash equilibrium in prisoners’ dilemmas or public goods games. In order to understand human morality and pro-social behaviour, we suggest that it would actually be more informative to investigate why we have not evolved to play the subgame perfect Nash equilibrium in sequential games, such as the ultimatum game and the trust game. The ‘rationally irrational’ behaviour that can evolve in such games gives a much better match with actual human behaviour, including elements of morality such as honesty, responsibility and sincerity, as well as the more hostile aspects of human nature, such as anger and vengefulness. The mechanism at work here is commitment, which does not need population structure, nor does it need interactions to be repeated. We argue that this shift in focus can not only help explain why humans have evolved to know wrong from right, but also why other animals, with similar population structures and similar rates of repetition, have not evolved similar moral sentiments. The suggestion that the evolutionary function of morality is to help us commit to otherwise irrational behaviour stems from the work of Robert Frank (Passions within reason: The strategic role of the emotions, 1988), which has played a surprisingly modest role in the scientific debate to date.
>> DIEP Seminar: Justus Uitermark (U. Amsterdam)
Beyond complexity | 14th of October 2021
Recent years have seen the emergence of a new interdisciplinary field for the study of social life: computational social science. Computational social scientists often draw upon methods and concepts developed within the natural sciences to examine patterns in social processes and identify underlying mechanisms. In this presentation, I document my engagement with computational social science, asking the overarching question how social life is different from the complex systems commonly studied in the natural sciences and what implications this should have for ontology and epistemology. I suggest that the complexity perspective may be useful but has difficulty incorporating quintessentially social phenomenon like meaning and power. By way of illustration, I discuss research I have done with computational scholars on social movements, public debates, cities, and the diffusion of scientific ideas.
Justus Uitermark is a sociologist and geographer who studies how different kinds of – online and offline – environments shape political and cultural conflict. He currently works as a professor of urban geography at the University of Amsterdam and is affiliated with the UvA’s Center for Urban Studies as well as the Amsterdam Institute for Social Science Research.
>> DIEP Seminar: Alexandru Baltag (U. Amsterdam)
Group (Ir)Rationality: can logic help? | 7th of October 2021
I present some applications of logical methods to the study of emergent phenomena in groups of `agents', capable of reflection, communication, reasoning, argumentation etc.The main focus is on the understanding of belief/preference formation and diffusion in social networks, and on how this affects the group's ``epistemic potential": the ability of the agents to track the truth of the matter (with respect to some given relevant topic). While in some cases, ``wisdom of the crowds" can increase the epistemic potential, in other situations the group's dynamics leads to informational distortions: the ``madness of the crowds": cascades, `groupthink', the curse of the committee, pluralistic ignorance, group polarization, doxastic cycles etc. I explain how logic (in combination with probabilistic methods) can be used to provide some explanations for both types of situations, as well as to suggest some partial solutions to informational distortions.
>> DIEP Seminar: Michel Mandjes (U. Amsterdam)
A diffusion-based analysis of a road traffic network | 30th of September 2021
In this talk I will discuss an important example of complex networks, namely road traffic networks. I start by giving an overview of the existing literature, distinguishing between microscopic models (describing the stochastic evolution of the position of individual vehicles) and macroscopic models (built around deterministic continuous flows, represented as partial differential equations). Then I argue that the “optimal" model is a compromise between these: we aim at a stochastic model with enough aggregation to make sure that explicit limiting analysis can be performed. The underlying dynamics are consistent with the macroscopic fundamental diagram that describes the functional relation between the vehicle density and velocity. Discretizing space, the model can be phrased in terms of a spatial population process, thus allowing the application of a classical scaling approach. More specifically, it follows that under a diffusion scaling, the vehicle density process can be approximated by an appropriate Gaussian process. This Gaussian approximation can be used to evaluate the travel time distribution between a given origin and destination. Based on joint work with Jaap Storm (VU).
>> DIEP Seminar: Federica Russo (U. Amsterdam)
Disease Causation and Public Health Interventions: the Entanglement of Conceptual, Methodological, and Normative Questions | 16th of September 2021
In this talk, I discuss how concepts, methods, and values are entangled. While the argument can be applied widely across the sciences, I focus here on the sciences of health and disease, and on public health. In particular, I will show that different ways of conceptualizing (disease) causation are inherently linked to the methods (used in the health sciences) and to values (at work e.g. in public health). I will argue, on top of well-established arguments, not only that scientific methods and concepts are value-laden, but also value-promoting, and so any normative questions cannot be asked at the end or outside of the scientific process, but should be an integral part of it.
>> DIEP Seminar: Maris Ozols (U. Amsterdam)
Introduction to quantum circuits | 24th of June 2021
Any Boolean function can in principle be decomposed into elementary logical gates, such as AND, OR and NOT, that act only on one or two bits at a time. Similarly, any operation on a quantum computer can be broken up into elementary gates that act only on one or two qubits at a time. I will explain how this works and what consequences this has for quantum algorithm design.
>> DIEP Seminar: Casper van Elteren (U. Amsterdam)
Through the looking glass - Information flows in complex systems | 10th of June 2021
Understanding dynamical systems is a fundamental problem for the 21st century.
Despite the prima facie differences and purposes of many real-world networks,
previous research shows several universal characteristics in networks properties
such as the small-world phenomenon, fat-tail degree and feedback loops. This has
lead to the common but often implicit assumption that the connectedness of a
node in the network is proportional to its dynamic importance. For example in
epidemic research, high degree nodes or "super-spreaders" are associated to
dominant epidemic risk and therefore deserve special attention. Yet prior
research shows that the shared universality in network characteristics is not
shared in the dynamic or functional properties of many real-world systems.
In this talk I will explore the relation between local interactions and
macroscopic properties of a system through the lens of statistical physics and
information theory. In particular, I will show novel methods on determining the
so-called driver node in complex systems, and how tipping point can be studied
from an information theoretical perspective.
>> DIEP Seminar: Velimir Ilić (Mathematical Institute of the Serbian Academy of Sciences and Arts)
An overview and characterization of generalized information measures | 3rd of June 2021
The aim of this talk is to present a comprehensive classification of the main entropic forms introduced in the last fifty years within statistical physics and information theory and to review the fundamental questions about the meaning of information. I will particularly focus on axiomatic approaches to the characterization of various generalizations of the Shannon entropy, such as the Rényi, the Tsallis, the Sharma-Mittal, and the Sharma-Mittal-Taneja entropies, as well as the more general classes of pseudo-additive entropies with a well-defined mathematical and information theoretic structure. Finally, I will point out possible applications of these measures in communication theory, statistical inference and complex systems modelling.
>> DIEP Seminar: Olivier Roy (U. Bayreuth)
Deliberation, Coherent Aggregation, and Anchoring | 27th of May 2021
In this talk we will present a number of results stemming from a computational model of collective attitude formation through a combination of group deliberation and aggregation. In this model the participants repeatedly exchange and update their preferences over small sets of alternatives, until they reach a stable preference profile. When they do so the collective attitude is computed by pairwise majority voting. The model shows, on the one hand, that rational preference change can fill an existing gap in known mechanisms purported to explain how deliberation can help avoiding incoherent group preferences. On the other hand, the model also reveals that when the participants are sufficiently biased towards their own opinion, deliberation can actually create incoherent group rankings, against the received view. The model suggests furthermore that rational deliberation can exhibit high levels of path dependencies or "anchoring", where the group opinion is strongly dependent on the order in which the participants contribute to the discussion. We will finish by discussing possible trade-offs between such positive and negative features of group deliberation.
>> DIEP Seminar: Sven Banisch (Max Planck Institute Leipzig)
Social Feedback Theory: Modeling collective opinion phenomena by learning from the feedback of others | 20th of May 2021
Humans are sensitive to social approval and disapproval. The feedback that others provide on our expressions of opinion is an important driver for adaptation and change. Social feedback theory provides a framework for modeling collective opinion processes based on these principles. The theory departs from previous models by differentiating an externally expressed opinion from an internal evaluation of it. Opinion dynamics is conceived as repeated games that agents play within their social network and to which they adapt by reward-based learning. Within this setting, game theoretic notions of equilibrium can be used to characterize structural conditions for qualitatively different regimes of collective opinion expression. In this talk, I aim for a broad perspective and discuss two models addressing emergent phenomena such as polarization and collective silence.
>> DIEP Seminar: Alan Kirman (Aix-Marseille University)
Crises in a complex world | 6th of May 2021
In the summer of 2019 three well known economists , Suresh Naidu, Dani Rodrik and Gabriel Zucman published an article in the Boston Review, entitled, “Economics After Neoliberalism” in which they argued that contemporary economics is finally breaking free from its market fetishism, offering plenty of tools we can use to make society more inclusive.
In response a number of us published, in the same journal, the following plea to go further.
“Our backgrounds are in economics, political science, psychology, anthropology, physics, computer science, evolutionary theory, and complex systems theory. To us, the phenomenon called “the economy” is a highly complex, multilevel system that encompasses human biology, human behavior, group behavior, institutions, technologies, and culture, all mutually entangled in networks of nonlinear, dynamic feedback. Each of these levels in the system is subject to learning, adaptation, evolutionary, and coevolutionary processes, which means that the system is constantly changing, self-creating, and never at rest. These dynamics in turn create system-level emergent behaviors, including economic growth, inequality, and financial booms and busts. The whole system, in turn, is deeply embedded in the physical processes of our planet.
This transdisciplinary perspective, sometimes referred to as “complexity economics,” differs in a number of significant ways from the traditional perspective of economics.”
Eric Beinhocker, W. Brian Arthur, Robert Axtell, Jenna Bednar, Jean-Philippe Bouchaud, David Colander, Molly Crockett, J. Doyne Farmer, Ricardo Hausmann, Cars Hommes, Alan Kirman, Scott Page, and David Sloan Wilson.
The purpose of my presentation is to show some of the ways in which complexity economics which views aggregate behaviour as emerging from the interaction between individuals and institutions can help us to understand the evolution of our socio-economic system. This will help us to break out of the restrictive framework in which crises are the result of exogenous shocks and lead us to think of crises as endogenous and arising from the way in which the system self organises.
>> DIEP Seminar: Jasper van Wezel (U. Amsterdam)
The non-Hermitian "split skin effect" | 15th of April 2021
From atomic chains, to lattices of cold atoms and metamaterials, non-reciprocal and non-conservative systems hosting waves can exhibit a dramatic phenomenon, known as the non-Hermitian skin effect in which all bulk modes are forced to one side of a finite system. Here, we demonstrate a driven mechanical chain that hosts a "split skin effect", in which an extensive fraction of the bulk modes localises on the side of the system opposite to the usual bulk mode localisation, and opposite to the driving in the chain.
This system realises a specific instance of a broad class of non-Hermitian, non-reciprocal systems in both classical and quantum mechanics, whose dynamics is governed by Toeplitz matrices. We present a theoretical analysis highlighting how both normal and split skin effect phases may arise in these systems, and how the localisation length of the various skin effect modes depends on the properties of the underlying Toeplitz matrix. Although our results clearly show the skin effect is not by itself topological in nature, we suggest an interpretation of the skin modes as topological edge modes of a hypothetical higher-dimensional system.
>> DIEP Seminar: Joris Mooij (U. Amsterdam)
How to learn causal relations from data | 22nd of April 2021
Many questions in science, policy making and everyday life are of a causal nature: how would a change of A affect B? Causal inference, a branch of statistics and machine learning, studies how cause-effect relationships can be discovered from data and how these can be used for making predictions in situations where a system has been perturbed by an external intervention. In this talk, I will introduce the basics of two, apparently quite different, approaches to causal discovery. I will discuss how both approaches can be elegantly combined in Joint Causal Inference (JCI), a novel constraint-based approach to causal discovery from multiple data sets. This approach leads to a significant increase in the accuracy and identifiability of the predicted causal relations. One of the remaining big challenges is how to scale up the current algorithms such that large-scale causal discovery becomes feasible.
>> DIEP Seminar: Mazi Jalaal (U. Amsterdam)
Light Production in a Unicellular Organism | 8th of April 2021
Bioluminescence (emission of light from living organisms) is a common form of communication in the ocean and land. It has evolved over forty times in history and can be found in multiple biological kingdoms like bacteria, fungi, and protozoa. Bioluminescence has been of interest to humankind for thousands of years and has been a source of commentary since ancient times, from Aristotle and Pliny the Elder to Shakespeare, Boyle and Darwin. While the internal biochemistry of light production by many organisms is well established, the manner by which fluid shear or mechanical forces trigger bioluminescence is still poorly understood. We will briefly review the history of the science of bioluminescence and then present our recent work on the bioluminescence of a single-celled organism, where we aim to understand the response (light production) to mechanical stimulation. We find a "viscoelastic" response in which light intensity depends on both the amplitude and rate of cell deformation, consistent with the action of stretch-activated ion channels. We also show how such a biological system can be modelled with a simple set of linear ordinary differential equations.
>> DIEP Seminar: Florian Wagener (U. Amsterdam)
How good risk management can cause financial crises | 1st of April 2021
Risk management is as old as finance. It is based on the observation that total risk can be reduced by spreading it out over market participants. However, new financial instruments are regularly at the root of global financial crises. We propose a mechanism how the availability of more financial instruments may destabilise markets when traders have heterogeneous expectations and adapt their behaviour according to performance-based reinforcement learning.
>> DIEP Seminar: Sebastian De Haro (U. Amsterdam)
Formulating Emergence in the Physical Sciences -- a Philosopher's Perspective | 25th of March 2021
An important problem in the philosophy of emergence is the different uses that different authors make of the word ‘emergence’, and of the distinctions that they draw between different kinds of emergence. In this talk, I will review recent proposals, especially by Butterfield, to define emergence as novelty of behaviour relative to an appropriate comparison class, and to clarify the relation between emergence, reduction, and supervenience. Then I will present my own proposal for how to best define emergence in the physical sciences.
>> DIEP Seminar: Mohsen Sadeghi (Freie Universität Berlin)
Large-scale dynamics of biomembranes and membrane-associated proteins | 18th of March 2021
Lipid bilayer membranes are self-assembled structures with the thickness of a few nanometers that can form an assortment of geometries of several micrometers in size, vital to the function of living cells. The so-called peripheral proteins, that can bind to the surface of membranes, are responsible for shaping and remodeling biomembranes. The necessary cooperative action of a multitude of curvature-inducing proteins leads to the emergence of the rich membrane geometries observed in living cells. In order to model this highly dynamic system close to its native spatiotemporal scales, a mesoscopic model that can accurately mimic membrane mechanics and solvent hydrodynamics is needed. In this talk, I present our coarse-grained dynamic membrane model , and the corresponding approach to hydrodynamics that leads to realistic membrane kinetics . I will talk about the entropic membrane-mediated interactions and investigate the kinetics, stationary distributions, and the free energy landscape governing the formation and break-up of protein clusters on the surface of the membrane.
 M. Sadeghi, T. R. Weikl, and F. Noé. Particle-based membrane model for mesoscopic simulation of cellular dynamics. J. Chem. Phys., 148(4):044901, 2018.
 Mohsen Sadeghi and Frank Noé. Large-scale simulation of biomembranes incorporating realistic kinetics into coarse-grained models. Nat. Commun., 11(1):2951, 2020.
>> DIEP Seminar: Clélia de Mulatier (U. Amsterdam)
Beyond pairwise model for binary data: the search for simple spin models| 11th of March 2021
Finding the model that best captures the patterns hidden within noisy data is a central problem in science. To address this issue, information theory and Bayesian statistics provide two comparable rigorous methods to select the best of potential explanations for data. The selected model is the one that achieves the optimal balance between goodness-of-fit and simplicity. Yet in practice, the computational cost associated with fitting each of the many potential models and the difficulty of evaluating model complexity make it challenging to search for “the” best model. Besides, with a finite amount of data an important limitation comes from the large degeneracy of models that perform nearly optimally.
In this talk I will discuss these issues in the context of binary data, where pairwise spin models (Ising model) are widely used. To understand the features of simple models, we will study the information theoretic complexity of spin models with interactions of arbitrary order, which form a complete family of candidate models for binary data. We will highlight the existence of transformations between models with interactions of different orders that preserve model complexity and see that, contrary to common intuition, pairwise models are not necessarily the simplest spin models.
We will finally discuss the development of new complementary methods of model selection for binary data that take into account high order interactions. In particular, we will discuss the use of minimally complex models for which all quantities of interest – the model complexity, the maximum likelihood, the evidence, and the Fisher information matrix – can be computed easily. This approach contrasts with the statistical inference of pairwise models for which maximum likelihood estimates are already computationally challenging. We will illustrate these techniques on several datasets.
>> DIEP Seminar: Wout Merbis (DIEP)
Exact epidemic models form a tensor product formulation | 24th of February 2021
A method for computing exact transition rate matrices for many well-known models of epidemic spreading on networks is presented. The state of the population is described as a tensor product of N individual probability vector spaces, with dimension equal to the number of compartments of the epidemiological model d. The transition rate matrix for the d^N-dimensional Markov chain is obtained by taking suitable linear combinations of tensor products of d-dimensional matrices. The resulting transition rate matrix is a sum over bilocal linear operators, which gives insight into the microscopic dynamics of the system. We show how the exact transition rate matrix for the susceptible-infected (SI) model can be used to find analytic solutions for SI outbreaks on finite trees and the cycle graph. We comment on possible applications of this formulation to the study of stochastic systems with many interacting constituents, such as the epidemic spreading process and other models of information spread on networks.
>> DIEP Seminar: Vítor Vasconcelos (U. Amsterdam)
Coordination in polarised societies| 18th of February 2021
Polarisation on various issues has increased in many western democracies since the 1980s. Beliefs about or observations of the behaviours and opinions of others drive individuals’ actions. These multiple social dynamics can support cooperative equilibria in the absence of enforcement by formal institutions, but they can also maintain harmful beliefs and behaviours. Through modelling and experiments, we explore the effects of polarisation on the likelihood that a society will coordinate on welfare-improving actions in a context where collective benefits are acquired only if enough individuals contribute—i.e., a coordination game. The talk will start with an analysis of competing complex-contagion processes and their role in generating different patterns in the distribution and segregation of ideas or opinions. Then, we will look into how heterogeneity of these opinions impacts collective action and the role of partial—and biased—information about others in improving the chances of collective success. Finally, we will show how different types of biases, not just the ones introduced by limited information but those intrinsic to human psychology, lead to suboptimal deadlocks.
>> DIEP Seminar: Clara Stegehuis (U. Twente)
Network structure and the spread of epidemics | 11th of February 2020
Many real-world networks contain groups of densely connected nodes, also called communities. We use random graph models to show that these community structures strongly influence the behavior of epidemic processes on networks: community structures can both enforce as well as inhibit epidemic processes. Our models further show that the exact internal structures of communities barely influence the behavior of percolation processes across networks. We then investigate how the final size of an epidemic is influenced by contact tracing and quarantining. We show that the effectiveness of such tracing processes strongly depends on the network structure. In contrast to previous findings, the tracing procedure is not necessarily more effective on networks with heterogeneous degrees. We also show that network clustering influences the effectiveness of the tracing process in a non-trivial way: depending on the infectiousness parameter, contact tracing on clustered networks may either be more, or less efficient than on networks without clustering.
>> DIEP Seminar: Janusz Meylahn (DIEP)
Algorithmic collusion using Q-learning | 4th of February
Algorithmic pricing is becoming more and more integrated into the marketplace. The danger of this, according to some economists and lawyers, is that the algorithms may learn to collude spontaneously. This would take the form of the algorithms charging higher prices than would be competitive. In the article we will discuss this week, Calvano et al. conduct simulated experiments with a Q-learning algorithm. They show that the use of the algorithm by two firms in a duopoly leads to supra-competitive prices without the algorithm being explicitly programmed to do so. Surprisingly, the algorithm seems to respond to an exogenous "defection" with a "punishment" followed by a return to the supra-competitive price. A strategy of this kind (Reward and Punishment Scheme) is thought to be a crucial ingredient for stable collusion. The result that such a strategy spontaneously emerges when using Q-learning has received sufficient attention to warrant a follow-up publication in Science.
>> DIEP Seminar: Soroush Rafiee Rad (DIEP)
On Correlated Information | 28th of January 2021
We discuss a paper by Alexandru Baltag and Sonja Smets on correlated knowledge: https://www.researchgate.net/publication/226943513_Correlated_Knowledge_An_Epistemic-Logic_View_on_Quantum_Entanglement
In this paper they model (classical and quantum) complex systems, and give a logical analysis of classical and quantum correlations using tools developed in the study of epistemic logics. They propose a logical system for reasoning about the information carried by a complex system consisting of different parts, and investigate the relationship between the information available in such a system as a whole and the information carried by each of its parts. In particular, their analysis distinguishes distributed information, that comes from pooling together all the information that can be observed in each separate part of the system, from correlated information, that is obtained by joint observations of the parts. This correlated information is only obtainable when the individual parts are combined and observed as a complex system, which allows information exchange between these parts. Similarly, for a set of individual agents with private information, correlated knowledge only emerges when they come together as a group, allowing for cooperation between them and the information dynamics that ensue. This is an instance of an emergent phenomena in information dynamics and epistemic logic and an example of logical analysis of such phenomena. This analysis elucidates the difference between classical and quantum information and provides an informational-logical characterization of 'quantum entanglement’.