How do such crowding problems arise, and could they be reduced? Some researchers believe that we can find the answers through a more familiar system in which jams appear – road traffic flow. Martin Treiber, of the Technical University of Dresden in Germany, has previously developed models for traffic flow, and now he has reported modifications that capture the essential details of sporting events such as marathons.
The generality of network properties allows the utilization of the ‘wisdom’ of biological systems surviving crisis events for many millions of years. Yeast protein-protein interaction network shows a decrease in community-overlap (an increase in community cohesion) in stress. Community rearrangement seems to be a cost-efficient, general crisis-management response of complex systems. Inter-community bridges, such as the highly dynamic ‘creative nodes’ emerge as crucial determinants helping crisis survival.
Crisis Responses and Crisis Management: what can we learn from Biological Networks? Péter Csermely, Agoston Mihalik, Zsolt Vassy, András London
Systema: connecting matter, life, culture and technology
Social, technological, and biological networks are known to organize into modules or “communities.” Characterizing and identifying modules is highly nontrivial and still an outstanding problem in networks research. A new approach uses both the concept of modular hierarchy for network construction and the methods of statistical inference to address this problem, succeeding where the existing approaches see difficulties.
Hierarchical Block Structures and High-Resolution Model Selection in Large Networks Tiago P. Peixoto Phys. Rev. X 4, 011047 (2014)
Biofilms are multifunctional and environmentally responsive assemblies of living and non-living components. By using synthetic gene networks in engineered cells to regulate the production of extracellular amyloid fibrils, and by interfacing the fibrils with inorganic materials such as metal nanoparticles, stimuli-responsive synthetic biofilms with switchable functions and tunable composition and structure have now been produced.
Synthesis and patterning of tunable multiscale materials with engineered cells • Allen Y. Chen, Zhengtao Deng, Amanda N. Billings, Urartu O. S. Seker, Michelle Y. Lu, Robert J. Citorik, Bijan Zakeri & Timothy K. Lu
The nonverbal transmission of information between social animals is a primary driving force behind their actions and, therefore, an important quantity to measure in animal behavior studies. Despite its key role in social behavior, the flow of information has only been inferred by correlating the actions of individuals with a simplifying assumption of linearity. In this paper, we leverage information-theoretic tools to relax this assumption. To demonstrate the feasibility of our approach, we focus on a robotics-based experimental paradigm, which affords consistent and controllable delivery of visual stimuli to zebrafish. Specifically, we use a robotic arm to maneuver a life-sized replica of a zebrafish in a predetermined trajectory as it interacts with a focal subject in a test tank. We track the fish and the replica through time and use the resulting trajectory data to measure the transfer entropy between the replica and the focal subject, which, in turn, is used to quantify one-directional information flow from the robot to the fish. In agreement with our expectations, we find that the information flow from the replica to the zebrafish is significantly more than the other way around. Notably, such information is specifically related to the response of the fish to the replica, whereby we observe that the information flow is reduced significantly if the motion of the replica is randomly delayed in a surrogate dataset. In addition, comparison with a control experiment, where the replica is replaced by a conspecific, shows that the information flow toward the focal fish is significantly more for a robotic than a live stimulus. These findings support the reliability of using transfer entropy as a measure of information flow, while providing indirect evidence for the efficacy of a robotics-based platform in animal behavioral studies.
Information Flow in Animal-Robot Interactions by Sachit Butail, Fabrizio Ladu, Davide Spinello and Maurizio Porfiri Entropy 2014, 16(3), 1315-1330; doi:10.3390/e16031315 http://www.mdpi.com/1099-4300/16/3/1315/
Evolutionary game theory has become one of the most diverse and far reaching theories in biology. Applications of this theory range from cell dynamics to social evolution. However, many applications make it clear that inherent non-linearities of natural systems need to be taken into account. One way of introducing such non-linearities into evolutionary games is by the inclusion of multiple players. An example is of social dilemmas, where group benefits could e.g.\ increase less than linear with the number of cooperators. Such multiplayer games can be introduced in all the fields where evolutionary game theory is already well established. However, the inclusion of non-linearities can help to advance the analysis of systems which are known to be complex, e.g. in the case of non-Mendelian inheritance. We review the diachronic theory and applications of multiplayer evolutionary games and present the current state of the field. Our aim is a summary of the theoretical results from well-mixed populations in infinite as well as finite populations. We also discuss examples from three fields where the theory has been successfully applied, ecology, social sciences and population genetics. In closing, we probe certain future directions which can be explored using the complexity of multiplayer games while preserving the promise of simplicity of evolutionary games.
Evolutionary Multiplayer Games Chaitanya S. Gokhale, Arne Traulsen
Spreading on networks is influenced by a number of factors including different parts of the inter-event time distribution (IETD), the topology of the network and nonstationarity. In order to understand the role of these factors we study the SI model on temporal networks with different aggregated topologies and different IETDs. Based on analytic calculations and numerical simulations, we show that if the stationary bursty process is governed by power-law IETD, the spreading can be slowed down or accelerated as compared to a Poisson process; the speed is determined by the short time behaviour, which in our model is controlled by the exponent. We demonstrate that finite, so called "locally tree-like" networks, like the Barab\'asi-Albert networks behave very differently from real tree graphs if the IETD is strongly fat-tailed, as the lack or presence of rare alternative paths modifies the spreading. A further important result is that the non-stationarity of the dynamics has a significant effect on the spreading speed for strongly fat-tailed power-law IETDs, thus bursty processes characterized by small power-law exponents can cause slow spreading in the stationary state but also very rapid spreading heavily depending on the age of the processes.
Spreading dynamics on networks: the role of burstiness, topology and stationarity Dávid X. Horváth, János Kertész
BIG data is suddenly everywhere. Everyone seems to be collecting it, analyzing it, making money from it and celebrating (or fearing) its powers. Whether we’re talking about analyzing zillions of Google search queries to predict flu outbreaks, or zillions of phone records to detect signs of terrorist activity, or zillions of airline stats to find the best time to buy plane tickets, big data is on the case. By combining the power of modern computing with the plentiful data of the digital era, it promises to solve virtually any problem — crime, public health, the evolution of grammar, the perils of dating — just by crunching the numbers.
We study explosive synchronization of network-coupled oscillators. Despite recent advances it remains unclear how robust explosive synchronization is in view of realistic structural and dynamical properties. Here we show that explosive synchronization can be induced simply by adding uncorrelated noise to the oscillators' frequencies, demonstrating it is not only robust to, but moreover promoted by, this natural mechanism. We support these results numerically and analytically, presenting simulations of a real neural network as well as a self consistency theory used to study synthetic networks.
Noise induces explosive synchronization Per Sebastian Skardal, Alex Arenas
We give a tutorial for the study of dynamical systems on networks, and we focus in particular on ``simple" situations that are tractable analytically. We briefly motivate why examining dynamical systems on networks is interesting and important. We then give several fascinating examples and discuss some theoretical results. We also discuss dynamical systems on dynamical (i.e., time-dependent) networks, overview software implementations, and give our outlook on the field.
Dynamical Systems on Networks: A Tutorial Mason A. Porter, James P. Gleeson
Modern infectious disease epidemiology has a strong history of using mathematics both for prediction and to gain a deeper understanding. However the study of infectious diseases is a highly interdisciplinary subject requiring insights from multiple disciplines, in particular a biological knowledge of the pathogen, a statistical description of the available data and a mathematical framework for prediction. Here we begin with the basic building blocks of infectious disease epidemiology—the SIS and SIR type models—before considering the progress that has been made over the recent decades and the challenges that lie ahead. Throughout we focus on the understanding that can be developed from relatively simple models, although accurate prediction will inevitably require far greater complexity beyond the scope of this review. In particular, we focus on three critical aspects of infectious disease models that we feel fundamentally shape their dynamics: heterogeneously structured populations, stochasticity and spatial structure. Throughout we relate the mathematical models and their results to a variety of real-world problems.
Evolutionary game theory is a successful mathematical framework geared towards understanding the selective pressures that affect the evolution of the strategies of agents engaged in interactions with potential conflicts. While a mathematical treatment of the costs and benefits of decisions can predict the optimal strategy in simple settings, more realistic situations (finite populations, non-vanishing mutations rates, communication between agents, and spatial interactions) require agent-based methods where each agent is modeled as an individual, carries its own genes that determine its decisions, and where the evolutionary outcome can only be ascertained by evolving the population of agents forward in time. Here we discuss the use of agent-based methods in evolutionary game theory and contrast standard results to those obtainable by a mathematical treatment. We conclude that agent-based methods can predict evolutionary outcomes where purely mathematical treatments cannot tread, but that mathematics is crucial to validate the computational simulations.
Evolutionary game theory using agent-based methods Christoph Adami, Jory Schossau, Arend Hintze
Our empirical analysis demonstrates that in the chosen network data sets, nodes which had a high Closeness Centrality also had a high Eccentricity Centrality. Likewise high Degree Centrality also correlated closely with a high Eigenvector Centrality. Whereas Betweenness Centrality varied according to network topology and did not demonstrate any noticeable pattern. In terms of identification of key nodes, we discovered that as compared with other centrality measures, Eigenvector and Eccentricity Centralities were better able to identify important nodes.
This article is based on the keynote address presented to the European Meetings on Cybernetics and Systems Research (EMCSR) in 2012, on the occasion of Edgar Morin receiving the Bertalanffy Prize in Complexity Thinking, awarded by the Bertalanffy Centre for the Study of Systems Science (BCSSS). The following theses will be elaborated on: (a) The whole is at the same time more and less than its parts; (b) We must abandon the term "object" for systems because all the objects are systems and parts of systems; (c) System and organization are the two faces of the same reality; (d) Eco-systems illustrate self-organization.
Complex Thinking for a Complex World – About Reductionism, Disjunction and Systemism Edgar Morin
Systema: connecting matter, life, culture and technology Vol 2, No 1 (2014)
Understanding why some human populations remain persistently poor remains a significant challenge for both the social and natural sciences. The extremely poor are generally reliant on their immediate natural resource base for subsistence and suffer high rates of mortality due to parasitic and infectious diseases. Economists have developed a range of models to explain persistent poverty, often characterized as poverty traps, but these rarely account for complex biophysical processes. In this Essay, we argue that by coupling insights from ecology and economics, we can begin to model and understand the complex dynamics that underlie the generation and maintenance of poverty traps, which can then be used to inform analyses and possible intervention policies. To illustrate the utility of this approach, we present a simple coupled model of infectious diseases and economic growth, where poverty traps emerge from nonlinear relationships determined by the number of pathogens in the system. These nonlinearities are comparable to those often incorporated into poverty trap models in the economics literature, but, importantly, here the mechanism is anchored in core ecological principles. Coupled models of this sort could be usefully developed in many economically important biophysical systems—such as agriculture, fisheries, nutrition, and land use change—to serve as foundations for deeper explorations of how fundamental ecological processes influence structural poverty and economic development.
Animal migrations span the globe, involving immense numbers of individuals from a wide range of taxa. Migrants transport nutrients, energy, and other organisms as they forage and are preyed upon throughout their journeys. These highly predictable, pulsed movements across large spatial scales render migration a potentially powerful yet underappreciated dimension of biodiversity that is intimately embedded within resident communities. We review examples from across the animal kingdom to distill fundamental processes by which migratory animals influence communities and ecosystems, demonstrating that they can uniquely alter energy flow, food-web topology and stability, trophic cascades, and the structure of metacommunities. Given the potential for migration to alter ecological networks worldwide, we suggest an integrative framework through which community dynamics and ecosystem functioning may explicitly consider animal migrations.
Migratory Animals Couple Biodiversity and Ecosystem Functioning Worldwide S. Bauer, B. J. Hoye
This article is an attempt to capture, in a reasonable space, some of the major developments and currents of thought in information theory and the relations between them. I have particularly tried to include changes in the views of key authors in the field. The domains addressed range from mathematical-categorial, philosophical and computational approaches to systems, causal-compositional, biological and religious approaches and messaging theory. I have related key concepts in each domain to my non-standard extension of logic to real processes that I call Logic in Reality (LIR). The result is not another attempt at a General Theory of Information such as that of Burgin, or a Unified Theory of Information like that of Hofkirchner. It is not a compendium of papers presented at a conference, more or less unified around a particular theme. It is rather a highly personal, limited synthesis which nonetheless may facilitate comparison of insights, including contradictory ones, from different lines of inquiry. As such, it may be an example of the concept proposed by Marijuan, still little developed, of the recombination of knowledge. Like the best of the work to which it refers, the finality of this synthesis is the possible contribution that an improved understanding of the nature and dynamics of information may make to the ethical development of the information society.
A real pendulum with friction will oscillate for a while after a short push but will eventually come to rest close to a location where is potential energy has a minimum. If the system is closed, that means, without a source of energy, it will eventually stop moving at a location near a minimum of the potential, no matter what type of friction force acts on the pendulum. This “variation principle” is a simple concept to predict the long-term behavior of mechanical systems, even if the details of the friction forces are unknown. For many years, scientist tried to find a similarly simple variation principle for systems with a source of energy such as a periodic forcing function or a battery in electrical systems . Prigogine suggested that time rate of entropy production is at a minimum at stationary states . Later the concept of entropy was generalized and used to describe the dynamics and stationary states of open dissipative systems [3-7].
Resilience of most critical infrastructures against failure of elements that appear insignificant is usually taken for granted. The World Airline Network (WAN) is an infrastructure that reduces the geographical gap between societies, both small and large, and brings forth economic gains. With the extensive use of a publicly maintained data set that contains information about airports and alternative connections between these airports, we empirically reveal that the WAN is a redundant and resilient network for long distance air travel, but otherwise breaks down completely due to removal of short and apparently insignificant connections. These short range connections with moderate number of passengers and alternate flights are the connections that keep remote parts of the world accessible. It is surprising, insofar as there exists a highly resilient and strongly connected core consisting of a small fraction of airports (around 2.3%) together with an extremely fragile star-like periphery. Yet, in spite of their relevance, more than 90% of the world airports are still interconnected upon removal of this core. With standard and unconventional removal measures we compare both empirical and topological perceptions for the fragmentation of the world. We identify how the WAN is organized into different classes of clusters based on the physical proximity of airports and analyze the consequence of this fragmentation.
Revealing the structure of the world airline network Trivik Verma, Nuno A. M. Araújo, Hans J Herrmann
Social networks have many counter-intuitive properties, including the "friendship paradox" that states, on average, your friends have more friends than you do. Recently, a variety of other paradoxes were demonstrated in online social networks. This paper explores the origins of these network paradoxes. Specifically, we ask whether they arise from mathematical properties of the networks or whether they have a behavioral origin. We show that sampling from heavy-tailed distributions always gives rise to a paradox in the mean, but not the median. We propose a strong form of network paradoxes, based on utilizing the median, and validate it empirically using data from two online social networks. Specifically, we show that for any user the majority of user's friends and followers have more friends, followers, etc. than the user, and that this cannot be explained by statistical properties of sampling. Next, we explore the behavioral origins of the paradoxes by using the shuffle test to remove correlations between node degrees and attributes. We find that paradoxes for the mean persist in the shuffled network, but not for the median. We demonstrate that strong paradoxes arise due to the assortativity of user attributes, including degree, and correlation between degree and attribute.
Network Weirdness: Exploring the Origins of Network Paradoxes Farshad Kooti, Nathan O. Hodas, Kristina Lerman
Almost universally, wealth is not distributed uniformly within societies or economies. Even though wealth data have been collected in various forms for centuries, the origins for the observed wealth-disparity and social inequality are not yet fully understood. Especially the impact and connections of human behavior on wealth could so far not be inferred from data. Here we study wealth data from the virtual economy of the massive multiplayer online game (MMOG) Pardus. This data not only contains every player's wealth at every point in time, but also all actions of every player over a timespan of almost a decade. We find that wealth distributions in the virtual world are very similar to those in western countries. In particular we find an approximate exponential for low wealth and a power-law tail. The Gini index is found to be g=0.65, which is close to the indices of many Western countries. We find that wealth-increase rates depend on the time when players entered the game. Players that entered the game early on tend to have remarkably higher wealth-increase rates than those who joined later. Studying the players' positions within their social networks, we find that the local position in the trade network is most relevant for wealth. Wealthy people have high in- and out-degree in the trade network, relatively low nearest-neighbor degree and a low clustering coefficient. Wealthy players have many mutual friendships and are socially well respected by others, but spend more time on business than on socializing. We find that players that are not organized within social groups with at least three members are significantly poorer on average. We observe that high `political' status and high wealth go hand in hand. Wealthy players have few personal enemies, but show animosity towards players that behave as public enemies.
Behavioral and Network Origins of Wealth Inequality: Insights from a Virtual World Benedikt Fuchs, Stefan Thurner
The confluence of new approaches in recording patterns of brain connectivity and quantitative analytic tools from network science has opened new avenues toward understanding the organization and function of brain networks. Descriptive network models of brain structural and functional connectivity have made several important contributions; for example, in the mapping of putative network hubs and network communities. Building on the importance of anatomical and functional interactions, network models have provided insight into the basic structures and mechanisms that enable integrative neural processes. Network models have also been instrumental in understanding the role of structural brain networks in generating spatially and temporally organized brain activity. Despite these contributions, network models are subject to limitations in methodology and interpretation, and they face many challenges as brain connectivity data sets continue to increase in detail and complexity.
In 1963-71, a group of people, myself included, formulated and perfected a new approach to physics problems, which eventually came to be known under the names of scaling, universality, and renormalization. This work formed the basis of a wide variety of theories ranging from its starting point in critical phenomena, and moving out to particle physics and relativity and then into economics and biology. This work was of transcendental beauty and of considerable intellectual importance. This left me with a personal problem. What next? Constructing the answer to that question would dominate the next 45 years of my professional life. I would try to: * Help in finding and constructing new fields of science * Do research and give talks on science/society borderline * Provide helpful, constructive criticism of scientific and technical work * Help students and younger scientists * Demonstrate scientific leadership
Innovations in Statistical Physics Leo P. Kadanoff
Noise permeates biology on all levels, from the most basic molecular, sub-cellular processes to the dynamics of tissues, organs, organisms and populations. The functional roles of noise in biological processes can vary greatly. Along with standard, entropy-increasing effects of producing random mutations, diversifying phenotypes in isogenic populations, limiting information capacity of signaling relays, it occasionally plays more surprising constructive roles by accelerating the pace of evolution, providing selective advantage in dynamic environments, enhancing intracellular transport of biomolecules and increasing information capacity of signaling pathways. This short review covers the recent progress in understanding mechanisms and effects of fluctuations in biological systems of different scales and the basic approaches to their mathematical modeling.