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| Scope | Program | Speakers | General Info | Local Info | Organizers | Funding | Application Form | Participants |
| Talks | ||
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Nathalie Questembert-Balaban Hebrew University |
Non-genetic variability in bacterial populations Biological processes are intrinsically noisy and often involve low numbers of molecules. Analysis of the variability in populations of genetically identical cells might shed light on the molecular interactions inside a cell, as well as on the evolutionary processes that have shaped these interactions. Our aim is to develop an experimental and theoretical framework for the quantitative study of phenotypic variation in populations of cells. We have developed new devices, based on microfluidic technology, in which single cells can be trapped and studied while controlling the environmental conditions. We now combine this technology with quantitative measurements of gene induction dynamics in single cells, to uncover the mechanism behind bacterial persistence, namely the heterogeneous response of bacteria to antibiotic treatment. Quantitative measurements lead to a simple mathematical description of the persistence switch, revealing its characteristic time scales. Beyond its relevance for the treatment of bacterial infections, the study of bacterial persistence constitutes a generic example of quantitative characterization of variability in populations of cells, and reveals the importance of noise in the dynamics of populations. |
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Christopher Barrett Bioinformatic Institute at VirginiaTech |
title abstract |
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Gyan Bhanot Rutgers |
Principal Component Analysis and Clustering Reveal Human Maternal Ancestry from Complete Mitochondrial Sequences We develop a simple, direct method to infer the phylogenetic tree for the maternal lineage of all humans using principal component analysis and consensus ensemble clustering. Unlike standard methods such as parsimony and maximum likelihood, our method is fast, gives a unique tree, makes no a-priori assumptions, uses all polymorphisms in the data and has high internal branch consensus. It confirms that modern humans came from Africa in at least two migrations and that the common maternal ancestor of humans or "mitochondrial Eve" lived in Africa ~200,000 years ago. It also suggests that the so called "R Clade", usually defined by a polymorphism at locus 12705 is too heterogeneous to have derived from a single common ancestor and places haplogroups B/R5/F in the Asian branch of the N Clade in agreement with their current location. |
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Ofer Biham Hebrew University |
How to simulate reaction networks with fluctuations Chemical, metabolic and ecological systems can be described by networks in which each node represents a species and each edge represents an interaction. The simulation of such networks is commonly done by rate equations, which are suitable when the populations of all species are large. However, when the populations are small, fluctuations become important and the rate equations fail. Stochastic methods such as direct integration of the master equation and Monte Carlo simulations should be applied. However, these methods are infeasible in the limit of large networks. In this talk I will present two approaches - the multiplane method and the moment equations, which enable highly efficient simulations of large stochastic netowrks. |
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David Bree ISI Foundation |
Energy policy: complex? Energy generation is comparatively simple, energy policy is not. Can complex systems science help us to understand the national and international policies past and present? Can it contribute to a sensible resolution of the problems caused by our low cost energy economies and resulting carbon emissions? If so, how? if not, why not? |
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Gregory John Chaitin IBM Watson Research Center |
Leibniz, Complexity and Incompleteness I will discuss Leibniz's ideas on complexity (Discours de metaphysique, 1686), leading to modern work on program-size complexity, the halting probability and incompleteness. Leibniz's principle of sufficient reason asserts that if anything is true it is true for a reason. But the bits of the numerical value of the halting probability are mathematical truths that are true for no reason. More precisely, as I will explain, they are irreducible mathematical truths, that is, true for no reason simpler than themselves. |
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Damien Challet ISI Foundation |
The broken symmetries of financial markets Various types of irregularities of financial markets will be discussed, together with the methods needed to characterise them and the models that reproduce them. A particular emphasis will be put on time reversal asymmetry. |
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Bernard Derrida Ecole Normale Supérieure |
random trees and genealogies The talk will review some of the statistical properties of the trees which represent the ancestry of evolving populations, both for neutral models of asexual and sexual reproduction. It will in particular show how the ages of the first common ancestors depend on the population size. |
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Santo Fortunato ISI Foundation |
Community structure in graphs Identifying communities in networks is an open challenge of fundamental importance in several disciplines. Here I discuss the main aspects of the problem, from the definition of community to the problem of hierarchy, including the crucial issue of testing methods of community detection. |
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Edoardo Gaffeo University of Trento |
Lévy-stable distribution in economics In this lecture I will provide an assessment of the role played by the class of Lévy-stable distributions in modern macroeconomics and econometrics. Some emphasis will be put on the distributional features of sectoral-level productivity growth rates, and their implications for macroeconomic theory. |
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Mauro Gallegati Universita Politecnica delle Marche |
Agent Based Models in Economics and Complexity A crucial aspect of the complexity approach is how interacting elements produce aggregate patterns that those elements in turn react to. This leads to the emergence of aggregate properties and structures that cannot be guessed by looking only at individual behaviour. Explicitly considering how heterogeneous elements dynamically develop their behaviour through interaction is a hard task analytically, the equilibrium analysis of mainstream (neoclassical) economics being a not neutral shortcut. On the other hand, explicitly considering the dynamics of the process started to be a feasible alternative only when computer power became widely accessible. The computational study of heterogeneous interaction agents is called agent-based modelling (ABM). Interestingly, among its first applications a prominent role was given to economic models, although it was quickly found of value in other disciplines too. Goal of this lecture is to motivate the use of the complexity approach and agent-based modelling in economics, by discussing the weaknesses of the traditional paradigm of mainstream economics, and then explain what ABM is and which research and policy questions it can help to analyse. |
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Shlomo Havlin Bar-Ilan University |
Statistical physics and complex networks Statistical physics approaches are developed and applied successfully in recent years to understand the topology, robustness and function of complex networks. We will show how ideas and tools from percolation theory lead to novel results on the robustness, immunization strategies, optimal paths and minimum spanning trees. These results are relevant to many real world systems ranging from the Internet to social systems and climate. |
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Lord Julian Hunt Centre for Polar Observation & Modelling |
Systems Modelling ; Approaches for making and exploring decisions Previous systems based analyses, predictions and decisions include conflict , environmental pollution, disease, organisational changes/issues and the mathematical methods involved - The big questions for system modellers working with decision makers are how to define and focus on key issues of optimal combination of micro and macro modelling |
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Janos Kertesz Budapest University of Technology and Economics |
Fluctuation scaling in complex systems: Taylor's law and beyond Complex systems consist of many interacting elements which participate in some dynamical process. The activity of various elements is often different and the fluctuation in the activity of an element grows monotonically with the average activity. This relationship is generically of the form fluctuations ≈ const.\times average^α, where the exponent α is predominantly in the range [1/2, 1]. This power law has been observed in a very wide range of disciplines, ranging from population dynamics through the Internet to the stock market and it is often treated under the names Taylor's law or fluctuation scaling. We attempt to show how general the above scaling relationship is by surveying the literature, as well as by reporting some new empirical data and model calculations. We also show some basic principles that can underlie the generality of the phenomenon. |
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Imre Kondor Collegium Budapest |
Irreducibility and Correlations in Complex Systems Biological, social, or economic complex systems are basically irreducible: they depend on a huge number of details. We argue that this is tantamount to saying that correlations in a complex system must be „critical” or „long ranged” in the sense that any component of the system must be strongly correlated with a large number of other components, though not necessarily with its geometrically close neighbours. This has far reaching consequences for the description of complex systems, and also for their numerical simulations. These ideas are illustrated on two toy models that encompass the elements of cooperation and competition: a random Boolean automaton and a spin glass model. |
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Yoram Louzoun Bar-Ilan University |
Dynamical properties of evolving networks Networks are usually studied as static objects, through the properties of a single snapshot of the network. The network generating mechanism are then deduced from the statistical properties of this snapshot. We propose a methodology to directly study the network evolution from dynamic data. This method joint with an appropriate Markov model for edge and node addition provides a direct insight on the network generation mechanism. The Markov model permits the quantitative comparison of the contribution of each generation mechanism to specific network properties. |
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Matteo Marsili ICTP |
The complexity of correlations between financial assets I discuss the properties of correlations between returns of financial assets, both static and dynamic, and the challenges they pose to understanding the dynamics of financial markets. Next I will discuss how these questions can be addressed in theoretical models, showing how these features are related to traders' behavior. |
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Ehud Meron Ben Gurion University |
Linking pattern formation and biodiversity in Dryland Vegetation Ecological processes generally involve different levels of organization, starting with a single species individual, through a population of many individuals of a given species, and up to a diverse community consisting of large populations of many different species interacting among themselves and with their physical environment. Upscaling low-level attributes, such as species traits and biomass-resource feedbacks, to community level properties, such as vegetation patterns and species diversity, is a highly challenging goal for theorists. In this talk I will describe recent progress our group has made towards achieving this goal in the context of dryland plant communities. |
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Andrzej Nowak University of Warsaw |
The alphabet model for rare events in social dynamics In most of social sciences prediction is based on extrapolation of central tendencies. In reality, almost everything that is important is the consequence of a rare event. The alphabet model describes how seemingly unimportant rare events may govern social dynamics. |
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Luciano Pietronero University of Rome "La Sapienza" |
COMPLEXITY: WHAT ARE WE TALKING ABOUT This field of physics was originally identified as Solid state Physics, then P.W. Anderson coined the term Condensed Matter Physics and more recently it has merged with Statistical Physics to lead to the Physics of Complex Systems. The study of complex systems refers to the emergency of collective properties in systems with a large number of parts in interaction among them. These elements can be atoms or macromolecules in a physical or biological context, but also people, machines or companies in a socio-economic context. The science of complexity tries to discover the nature of the emerging behavior of complex systems, often invisible to the traditional approach, by focusing on the structure of the interconnections and the general architecture of systems, rather than on the individual components. It is a change of perspective in the forma mentis of scientists rather than a new scientific discipline. Traditional science is based on a reductionistic reasoning for which, if one knows the basic elements of a system, it is possible to predict its behavior and properties. It is easy to realize, however, that for a cell or for the socio-economic dynamics one faces a new situation in which the knowledge of the individual parts is not sufficient to describe the global behavior of the structure. We can represent this situation as the study of the architecture of matter and nature. It depends in some way from the individual elements (bricks) but then it shows fundamental laws and properties which cannot be derived from these elements. Starting from the simplest physical systems, like critical phenomena in which order and disorder compete, these emergent behaviors can be identified in many other systems, from ecology to the immunitary system, to the social behavior and economics. The science of complexity has the objective of understand the properties of these systems. Which rules govern their behavior? How they adapt to changing conditions? How they learn efficiently and how they optimize their behavior? The development of the science of complexity cannot be reduced to a single theoretical or technological innovation but it implies a novel scientific approach with enormous potentialities to influence deeply the scientific activities, social, economic and technological. |
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Daniel Segre Boston University |
Adaptation and organization in the economy of living matter Metabolic networks guarantee the supply of energy and building blocks necessary for the maintenance of life. Using genomic information, mathematical models, and optimality criteria, one can learn about their evolutionary history and organization principles. |
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Nir Shaviv Hebrew University |
Complexity and Complications in Climate Physics Climate physics is complicated. There are many physical ingredients and many unknowns, making any prediction problematic. I will use the example of global warming to demonstrate just how much. To understand the problem, questions ranging from radiative transfer to the behavior of ecological and even economic systems must be answered. One such example, directly related to the topic of the workshop, is the behavior and structure of convective cloud cells. |
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Nadav Shnerb Bar-Ilan University |
Stabilization of metapopulation cycles: Toward a classification scheme The stability of population oscillations in ecological systems is considered. Experiments suggest that in many cases the single patch dynamics of predator prey or host-parasite systems is extinction prone and stability is achieved only when the spatial structure of the population is expressed via desynchronization between patches. A few mechanisms have been suggested so far to explain the inability of dispersal to synchronize the system. We suggest a classification scheme that allows for either a-priori (based on the system parameters) or a posteriori (based on local measurements) identification of the dominant process that yields desynchronization. |
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Ricard V. Sole Universitat Pompeu Fabra |
Emergence of complexity in biological networks: from selection to tinkering Recent work has been searching for general principles of organization and evolution of natural and artificial systems changing through local rules based on reuse of previously existing substructures. Such a process of "tinkering" makes a big difference (at least in principle) when comparing biological structures and man-made artifacts. As pointed out by the French biologist François Jacob, the engineer is able to foresee the future use of the artifact (i.e. it acts as a designer) whereas evolution does not. The first can ignore previous designs, whereas the second is based on changes taking place by using available structures. In spite of its apparent drawbacks, tinkering has been able to generate most complex structures observable in the real world (including some in the technological world). Very often, the resulting structures share common principles of organization, suggesting that convergent evolution towards a limited number of basic plans is inevitable. How innovations emerge through evolution is one of the key problems in complexity. Recent work on evolved complex networks suggests that tinkering is a main driving force shaping complex systems and that several desirable properties, including modularity, might emerge for free under tinkered evolution. |
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Dietrich Stauffer University of Cologne |
The Schelling model of urban segregation The later economics Nobel laureate Schelling published in 1971 a model for the spontaneous segregation of people of two different groups (ethnic, religious, ...). Recent simulations of the Ising-type model are reviewed. |
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Gérard Weisbuch Ecole Normale Supérieure |
Patterns in socio-economics Scale free distributions as result of noisy multiplicative processes have been widely discussed recently; their counter-part in spatially extended systems are spatio-temporal patterns of the same kind as discussed by Turing in 1952 (reaction-diffusion systems). I will describe their application to geographical economics after some recall of earlier applications and methods. |
| Student Talks | |
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| Omri Allouch Hebrew University |
The Markovian Patch-Occupancy (MPO) framework in Community Ecology Ecological research over the years has pointed to the existence of a wide spectrum of 'semi-universal' patterns of species diversity, found over very different life forms and ecosystems (e.g., the species-area relationship, the productivity-diversity relationship, the local-regional diversity relationship, etc.). We present the Markovian Patch-Occupancy (MPO) framework as a powerful platform for analyzing the mechanisms underlying these patterns. The MPO framework uses a stochastic individual-based model of an ecological community, based on the theory of Markov processes. The analytically-tractable model is both general and highly flexible, and can easily incorporate a wide spectrum of ecological factors including the effects of area, geographical isolation, habitat loss, habitat heterogeneity, life-history characteristics and trade-offs, density dependence, community-level carrying capacity, competition for space, various forms of dispersal (random dispersal, preference for unoccupied sites, preference for suitable habitats), and complete flexibility in the demographic rates of individual species. The MPO framework can be used to formulate and solve modern models of the neutral theory, and is capable of explaining a surprisingly wide spectrum of the 'semi-universal' patterns of species diversity. The generality, high flexibility and analytic tractability of the MPO framework make it a powerful platform for other research fields as well. |
| Michael Assaf Hebrew University |
Noise enhanced persistence in regulatory networks with feedback control Regulatory networks, describing e.g., intracellular biochemical reactions, may display significant internal noise. The noise emerges due to the discreteness of the reagents and the stochastic nature of the reactions, and becomes significant when the relevant species are present in low copy numbers. In many reaction schemes where influx of particles is absent, the final state of the system is a state with no particles - extinction. This phenomenon is absent in the deterministic rate-equation picture, where noise is ignored. As extinction occurs via a rare sequence of decay events, the mean time to extinction of the species may be long. Nevertheless, predicting it accurately can be important, e.g., when the relevant species has an essential function in a cell, and its extinction may eventually lead to the death of the cell. In this work we investigate a prototypical two-species regulatory network with negative feedback, where an autocatalytic growth of a regulated component is inhibited by signal molecules. Such negative feedback loops are ubiquitous in biology and are crucial to keep systems away from undesired states. We find that, counter-intuitively, the noise in the number of signal molecules can greatly delay the extinction of the regulated component. In addition, we determine the two-dimensional probability distribution function of the two species and show that deterministic rate equations may fail in predicting the average number of species even when this number is large, and the time is short compared to the mean time to extinction of the regulated component. |
| Simona Cantono University of Torino |
Information feedback mechanism and market dominance in a percolation model of eco-innovation diffusion New technologies often enter the market at a competitive disadvantage. While they may seem to promise future advantages such as lower costs, environmental friendliness, or higher performance, initially they may be significantly more expensive than incumbent technologies or face teething problems. The adoption of a new technology may also be affected by consumers’ uncertainty on its performance. In such an environment, information feedbacks are likely to arise and could allow a certain technology to be the dominant one in the market. Drawing on recent percolation models of diffusion that combine the contagion aspect of agents distributed on a network, with the heterogeneity of agent characteristics, we develop a complex-dynamics model of new technology diffusion. By using a multinomial decision mechanism to model each adopter’s choice on a portfolio of new available technologies we show the effect of information feedbacks on market dominance. Using agent-based simulations we explore when a limited subsidy policy, combined with a power-law learning curve for the price as a function of the cumulative number of adopters, can trigger a self-sustained diffusion of a certain technology. |
| Esteban Guevara Universita degli Studi dell'Insubria and SION |
Common Welfare, Strong Currencies and the Globalization Process The so called “globalization” process (i.e. the inexorable integration of markets, currencies, nation-states, technologies andthe intensification of consciousness of the world as a whole) has a behavior exactly equivalent to a system that is tending to a maximum entropy state. This globalization process obeys a collective welfare principle in where the maximum payoff is given by the equilibrium of the system and its stability by the maximization of the welfare of the collective besides the individual welfare. This let us predict the apparition of big common markets and strong common currencies. They will reach the “equilibrium” by decreasing its number until they reach a state characterized by only one common currency and only one big common community around the world. |
| Santiago Gil Fritz-Haber-Institut |
Dynamic networks at the edge of chaos A network of coupled phase oscillators is considered. Interactions between the oscillators are characterized by phase shifts, effectively taking into account interaction delays. We show that in this simple model coherent collective dynamics can emerge. Alternatively, chaos can develop when interaction phase shifts are large enough. Introducing a global feedback, chaotic behavior can be suppressed, giving rise to localized structures in the network with complex dynamical behavior. This transition scenario is analyzed, and special attention is paid to the dynamical properties of self-organized structures. |
| Salomon Israel Hebrew University |
Association between genetic polymorphisms for vasopressin and oxytocin receptors and pro-social behavior in economic decision tasks Human altruism is a widespread phenomenon that has puzzled evolutionary biologists since Darwin. Economic games illustrate human altruism by demonstrating that behavior deviates from economic predictions of selfish utility maximization. A game that most plainly demonstrates this altruistic tendency is the Dictator Game. We hypothesized that human pro-social behavior is to some extent hardwired and that two likely candidate genes that may contribute to individual differences in altruistic behavior are the arginine vasopressin receptor (AVPR1a) and the oxytocin receptor (OXTR). Genes that in some mammals such as the vole have been shown to have a profound impact on affiliative behaviors. Multiple studies have shown how the two closely related neuropeptides facilitate social communication, and cognition across mammals; in the current investigation, we demonstrate that AVPR1a and OXTR polymorphisms predict pro-social allocation of funds in two economic games that measure altruistic and pro-social behavior, the Dictator Game and Social Value Orientations (SVO). 203 college students participated in both a one-time online version of the Dictator game and SVO. Subjects and their parents were also genotyped for the AVPR1a and OXTR. Using a family-based method, we observed preferential transmission of individual alleles for the Dictator game and the SVO. |
| Karolina Lisiecka University of Warsaw |
Nowak-Vallacher's Mouse Paradigm - a tool for measuring the dynamics of thought Traditional approaches in social psychology attribute attitude change to external factors disregarding the intrinsic dynamics of information processing in the brain. Vallacher and Nowak proposed a method for measuring momentary changes in the stream of thought, the Mouse Paradigm. In this approach, momentary state of one's feelings about an object corresponds to the perceived distance from this object. By tracking the computer mouse movements produced by subjects thinking about the object, it is possible to find temporal patterns of attitude change. In the presentation it will be argued that the Mouse Paradigm can be a valuable tool for measuring social judgment and self-esteem. |
| Alon Manor Bar Ilan University |
Facilitation, competition, and vegetation patchiness: From scale free distribution to patterns A new technique for the modeling of perennial vegetation patchiness in the arid/semiarid climatic zone is suggested. Incorporating the stochasticity that affects life history of seedlings and the deterministic dynamics of soil moisture and biomass, this model is flexible enough to yield qualitatively different forms of spatial organization. In the facilitation-dominated regime, scale free distribution of patch sizes is observed, in correspondence with recent field studies. In the competition controlled case, on the other hand, power-law statistics is valid up to a cutoff, and an intrinsic length scale appears. |
| Yosef Maruvka Bar Ilan University |
Global Features Of Species Network A simple model for competition induced speciation is presented and analyzed. Logistic growth with nonlocal interaction is studied on regular and random networks, and the large scale structure of the emerging genomic frequencies is examined. The neutrality assumption is violated if the network is random and the competition is nonlocal. Instead, "Hubs" in the sequence space are suppressed by the competition more than nodes of lower degree. Thus, speciation is unavoidable for large scale free networks. The emerging genetic mixture depends strongly on the initial conditions. The frequency of hubs is much larger when the population evolves from a single nucleation event, in comparison with populations that recover from a catastrophe. |
| Animesh Mukherjee IIT Kharagpur |
Self-Organization of Sound Systems In the framework of Complex Networks The sound inventories of the world's languages show a considerable extent of symmetry. It has been postulated that this symmetry is a reflection of the human physiological, cognitive and societal factors. Although the organization of the vowel systems has been satisfactorily explained for smaller inventories, the structure of the consonant inventories is an open problem since 1939. We reformulate the problem in the light of statistical physics, more precisely complex networks, and observe that the distribution of the occurrence and co-occurrence of the phonemes (consonants and vowels) over languages are scale-free. The co-occurrence network exhibits strong community structures, where the driving forces behind the community formation are the human articulatory and perceptual factors. In order to validate the above principle, we introduce an information theoretic definition of these factors - feature entropy and feature distance - and show that the natural language inventories are significantly different in these terms from the randomly generated ones. A preferential attachment based growth model can lead to the emergence of similar topologies as that of the real networks. Furthermore, in a separate study, we observe that spectral analysis of the co-occurrence network of consonants helps us in the induction of linguistic typologies. |
| Stefan Wieland Instituto Gulbenkian de Ciencia, Oeiras, Portugal |
The coupling of strain evolution and disease dynamics Influenza A is characterized by seasonal outbreaks and a gradual genetic, yet discontinuous antigenic evolution (antigenic "cluster jumps" occuring every 4-7 years, with a large seasonal epidemic as a result of lacking immunity among the population).The interplay between viral mutations and an age-group-specific human immune response is modelled here, trying to account for the observed phenomena and to predict next season's epidemic strain from the dynamics of the previous year. |