To find useful work to do for their colony, individual eusocial animals have to move, somehow staying attentive to relevant social information. Recent research on individual Temnothorax albipennis ants moving inside their colony’s nest found a power-law relationship between a movement’s duration and its average speed; and a universal speed profile for movements showing that they mostly fluctuate around a constant average speed. From this predictability it was inferred that movement durations are somehow determined before the movement itself. Here, we find similar results in lone T. albipennis ants exploring a large arena outside the nest, both when the arena is clean and when it contains chemical information left by previous nest-mates. This implies that these movement characteristics originate from the same individual neural and/or physiological mechanism(s), operating without immediate regard to social influences. However, the presence of pheromones and/or other cues was found to affect the inter-event speed correlations. Hence we suggest that ants’ motor planning results in intermittent response to the social environment: movement duration is adjusted in response to social information only between movements, not during them. This environmentally flexible, intermittently responsive movement behaviour points towards a spatially allocated division of labour in this species. It also prompts more general questions on collective animal movement and the role of intermittent causation from higher to lower organizational levels in the stability of complex systems.
In quantum many-body physics, the spectral gap is the energy difference between the ground state of a system and its first excited state. Establishing whether it is possible to make a decision about the system being gapped or gapless, given a specific model Hamiltonian, is a long-standing problem in physics known as the spectral gap problem. Here, Toby Cubitt et al. prove that the spectral gap problem is undecidable. Although it had been known before that deciding about the existence of a spectral gap is difficult, this result proves the strongest possible form of algorithmic difficulty for a core problem of many-body physics.
Undecidability of the spectral gap Toby S. Cubitt, David Perez-Garcia & Michael M. Wolf
We investigate the sensorimotor loop of simple robots simulated within the LPZRobots environment from the point of view of dynamical systems theory. For a robot with a cylindrical shaped body and an actuator controlled by a single proprioceptual neuron, we find various types of periodic motions in terms of stable limit cycles. These are self-organized in the sense that the dynamics of the actuator kicks in only, for a certain range of parameters, when the barrel is already rolling, stopping otherwise. The stability of the resulting rolling motions terminates generally, as a function of the control parameters, at points where fold bifurcations of limit cycles occur. We find that several branches of motion types exist for the same parameters, in terms of the relative frequencies of the barrel and of the actuator, having each their respective basins of attractions in terms of initial conditions. For low drivings stable limit cycles describing periodic and drifting back-and-forth motions are found additionally. These modes allow to generate symmetry breaking explorative behavior purely by the timing of an otherwise neutral signal with respect to the cyclic back-and-forth motion of the robot.
The Sensorimotor Loop as a Dynamical System: How Regular Motion Primitives May Emerge from Self-Organized Limit Cycles Bulcsú Sándor, Tim Jahn, Laura Martin and Claudius Gros
Cooperation is powerful. There aren’t many highly cooperative species–but they nearly cover the planet. Ants alone account for a quarter of all animal matter. Yet the human capacity to work together leaves every other species standing. We organize ourselves into communities of hundreds of millions of individuals, inhabit every continent, and send people into space. Human beings are nature’s greatest team players. And the truly astounding thing is, we only started our steep climb to the top of the rankings–overtaking wasps, bees, termites and ants–in the last 10,000 years. Genetic evolution can’t explain this anomaly. Something else is going on. How did we become the ultrasocial animal? In his latest book, the evolutionary scientist Peter Turchin (War and Peace and War) solves the puzzle using some astonishing results in the new science of Cultural Evolution. The story of humanity, from the first scattered bands of Homo sapiens right through to the greatest empires in history, turns out to be driven by a remorseless logic. Our apparently miraculous powers of cooperation were forged in the fires of war. Only conflict, escalating in scale and severity, can explain the extraordinary shifts in human society–and society is the greatest military technology of all. Seen through the eyes of Cultural Evolution, human history reveals a strange, paradoxical pattern. Early humans were much more egalitarian than other primates, ruthlessly eliminating any upstart who wanted to become alpha male. But if human nature favors equality, how did the blood-soaked god kings of antiquity ever manage to claim their thrones? And how, over the course of thousands of years, did they vanish from the earth, swept away by a reborn spirit of human equality? Why is the story of human justice a chronicle of millennia-long reversals? Once again, the science points to just one explanation: war created the terrible majesty of kingship, and war obliterated it. Is endless war, then, our fate? Or might society one day evolve beyond it? There’s only one way to answer that question. Follow Turchin on an epic journey through time, and discover something that generations of historians thought impossible: the hidden laws of history itself.
We report the appearance of chimera states in a minimal extension of the classical Vicsek model for collective motion of self-propelled particle systems. Inspired by earlier works on chimera states in the Kuramoto model, we introduce a phase lag parameter in the particle alignment dynamics. Compared to the oscillatory networks with fixed site positions, the self-propelled particle systems can give rise to distinct forms of chimeras resembling moving flocks through an incoherent surrounding, for which we characterize their parameter domains. More specifically, we detect localized directional one-headed and multi-headed chimera states, as well as scattered directional chimeras without space localization. We discuss canonical generalizations of the elementary Vicsek model and show chimera states for them indicating the universality of this novel behavior. A continuum limit of the particle system is derived that preserves the chimeric behavior.
Self-propelled Chimeras Nikita Kruk, Yuri Maistrenko, Nicolas Wenzel, Heinz Koeppl
In the current hyper-connected era, modern Information and Communication Technology systems form sophisticated networks where not only do people interact with other people, but also machines take an increasingly visible and participatory role. Such human-machine networks (HMNs) are embedded in the daily lives of people, both or personal and professional use. They can have a significant impact by producing synergy and innovations. The challenge in designing successful HMNs is that they cannot be developed and implemented in the same manner as networks of machines nodes alone, nor following a wholly human-centric view of the network. The problem requires an interdisciplinary approach. Here, we review current research of relevance to HMNs across many disciplines. Extending the previous theoretical concepts of socio-technical systems, actor-network theory, and social machines, we concentrate on the interactions among humans and between humans and machines. We identify eight types of HMNs: public-resource computing, crowdsourcing, web search engines, crowdsensing, online markets, social media, multiplayer online games and virtual worlds, and mass collaboration. We systematically select literature on each of these types and review it with a focus on implications for designing HMNs. Moreover, we discuss risks associated with HMNs and identify emerging design and development trends.
Understanding Human-Machine Networks: A Cross-Disciplinary Survey Milena Tsvetkova, Taha Yasseri, Eric T. Meyer, J. Brian Pickering, Vegard Engen, Paul Walland, Marika Lüders, Asbjørn Følstad, George Bravos
Surprising advances are being achieved, for example in 'deep learning' — a method for approximating complex functions using thousands of numerical parameters. And robots are evolving, with advances in 3D sensing and mapping. But progress is not nearly as steady as some claim. Three books explore the topic from different perspectives. All suggest that robot superiority faces a formidable obstacle: human psychology.
The Network Science Institute was born out of our commitment to explore universality and predictability of systems to discover their function and develop intervention strategies to improve the health and security of human populations. The Institute is a highly collaborative and interdisciplinary lab space driven by the need to integrate models, theories and problem solving approaches across disciplines in research and education.
Inclusive Fitness Theory (IFT) was proposed half a century ago by W.D. Hamilton to explain the emergence and maintenance of cooperation between individuals that allows the existence of society. Contemporary evolutionary ecology identified several factors that increase inclusive fitness, in addition to kin-selection, such as assortation or homophily, and social synergies triggered by cooperation. Here we propose an Extend Inclusive Fitness Theory (EIFT) that includes in the fitness calculation all direct and indirect benefits an agent obtains by its own actions, and through interactions with kin and with genetically unrelated individuals. This formulation focuses on the sustainable cost/benefit threshold ratio of cooperation and on the probability of agents sharing mutually compatible memes or genes. This broader description of the nature of social dynamics allows to compare the evolution of cooperation among kin and non-kin, intra- and inter-specific cooperation, co-evolution, the emergence of symbioses, of social synergies, and the emergence of division of labor. EIFT promotes interdisciplinary cross fertilization of ideas by allowing to describe the role for division of labor in the emergence of social synergies, providing an integrated framework for the study of both, biological evolution of social behavior and economic market dynamics.
Extended Inclusive Fitness Theory bridges Economics and Biology through a common understanding of Social Synergy Klaus Jaffe
The workshop is aimed at discussing a few chosen contemporary developments in statistical physics. Topics include problems in condensed matter and dynamical systems (pattern structures, granular matter, glass formation, turbulence, marginal chaos, etc.); and also current applications outside of traditional fields in physics (in biology, ecology, sociology, economy, seismology and other geophysical, astrophysical phenomena, complexity in urban developments, complexity in linguistics, literature and arts, etc.). There would be an examination of equilibrium and nonequilibrium theories, and of the current efforts in generalizing statistical mechanical structures and methods. We would like to emphasize that our aim is to make the meeting the occasion for a memorable scientific discussion that can be carried out comfortably in an intimate environment.
International Workshop on Nonlinearity, Nonequilibrium and Complexity: Questions and perspectives in Statistical Physics. This is an event in honor of Prof. Alberto Robledo's 70th birthday.
Human societies use complexity -- within their institutions and technologies -- to address their various problems, and they need high-quality energy to create and sustain this complexity. But now greater complexity is producing diminishing returns in wellbeing, while the energetic cost of key sources of energy is rising fast. Simultaneously, humankind's problems are becoming vastly harder, which requires societies to deliver yet more complexity and thus consume yet more energy. Resolving this paradox is the central challenge of the 21st century. Thomas Homer-Dixon holds the CIGI Chair of Global Systems at the Balsillie School of International Affairs in Waterloo, Canada, and is a Professor at the University of Waterloo.
Three forms of creativity are exemplified in biology and studied in ALife. Combinational creativity exists as the first step in genetic algorithms. Exploratory creativity is seen in models using cellular automata or evolutionary programs. Transformational creativity can result from evolutionary programming. Even radically novel forms can do so, given input from outside the program itself. Transformational creativity appears also in reaction-diffusion models of morphogenesis. That there are limits to biological creativity is suggested by ALife work bearing on instances of biological impossibility.
Consensus dynamics in decentralised multiagent systems are subject to intense studies, and several different models have been proposed and analysed. Among these, the naming game stands out for its simplicity and applicability to a wide range of phenomena and applications, from semiotics to engineering. Despite the wide range of studies available, the implementation of theoretical models in real distributed systems is not always straightforward, as the physical platform imposes several constraints that may have a bearing on the consensus dynamics. In this paper, we investigate the effects of an implementation of the naming game for the kilobot robotic platform, in which we consider concurrent execution of games and physical interferences. Consensus dynamics are analysed in the light of the continuously evolving communication network created by the robots, highlighting how the different regimes crucially depend on the robot density and on their ability to spread widely in the experimental arena. We find that physical interferences reduce the benefits resulting from robot mobility in terms of consensus time, but also result in lower cognitive load for individual agents.
Emergence of Consensus in a Multi-Robot Network: from Abstract Models to Empirical Validation Vito Trianni, Daniele De Simone, Andreagiovanni Reina, Andrea Baronchelli
We study the empirical relationship between democracy and growth using grid-based panel regression and regime-transition frameworks. Our set-up nests several existing approaches, such as Barro (1996) and Gerring et al. (2005), and reconciles their conflicting messages in a more general model, and we identify the best-fitting discounts and memories. Our main finding is that democracy --best-modelled as a stock variable-- does cause growth, especially beyond the immediate short-run, by enabling the accumulation of physical, human, social and political capitals. Beyond threshold levels of democratic and economic development, however, there are incentives for de-democratization in order to boost short-run growth at the cost of higher sustained long-run growth.
Democracy-Growth Dynamics for Richer and Poorer Countries
Biological organisms have evolved a wide range of immune mechanisms to defend themselves against pathogens. Beyond molecular details, these mechanisms differ in how protection is acquired, processed and passed on to subsequent generations -- differences that may be essential to long-term survival. Here, we introduce a mathematical framework to compare the long-term adaptation of populations as a function of the pathogen dynamics that they experience and of the immune strategy that they adopt. We find that the two key determinants of an optimal immune strategy are the frequency and the characteristic timescale of the pathogens. Depending on these two parameters, our framework identifies distinct modes of immunity, including adaptive, innate, bet-hedging and CRISPR-like immunities, which recapitulate the diversity of natural immune systems.
Diversity of immune strategies explained by adaptation to pathogen statistics Andreas Mayer, Thierry Mora, Olivier Rivoire, Aleksandra M. Walczak
Predicting the future evolution of complex systems is one of the main challenges in complexity science. Based on a current snapshot of a network, link prediction algorithms aim to predict its future evolution. We apply here link prediction algorithms to data on the international trade between countries. This data can be represented as a complex network where links connect countries with the products that they export. Link prediction techniques based on heat and mass diffusion processes are employed to obtain predictions for products exported in the future. These baseline predictions are improved using a recent metric of country fitness and product similarity. The overall best results are achieved with a newly developed metric of product similarity which takes advantage of causality in the network evolution.
Prediction in complex systems: the case of the international trade network Alexandre Vidmer, An Zeng, Matúš Medo, Yi-Cheng Zhang
The spreading of information is of crucial importance for the modern information society. While we still receive information from mass media and other non-personalized sources, online social networks and influence of friends have become important personalized sources of information. This calls for metrics to measure the influence of users on the behavior of their friends. We demonstrate that the currently existing metrics of friends’ influence are biased by the presence of highly popular items in the data, and as a result can lead to an illusion of friends influence where there is none. We correct for this bias and develop three metrics that allow to distinguish the influence of friends from the effects of item popularity, and apply the metrics on real datasets. We use a simple network model based on the influence of friends and preferential attachment to illustrate the performance of our metrics at different levels of friends’ influence.
Unbiased metrics of friends’ influence in multi-level networks Alexandre Vidmer, Matúš Medo and Yi-Cheng Zhang
Inspired by Adam Smith and Friedrich Hayek, many economists have postulated the existence of invisible forces that drive economic markets. These market forces interact in complex ways making it difficult to visualize or understand the interactions in every detail. Here I show how these forces can transcend a zero-sum game and become a win-win business interaction, thanks to emergent social synergies triggered by division of labor. Computer simulations with the model Sociodynamica show here the detailed dynamics underlying this phenomenon in a simple virtual economy. In these simulations, independent agents act in an economy exploiting and trading two different goods in a heterogeneous environment. All and each of the various forces and individuals were tracked continuously, allowing to unveil a synergistic effect on economic output produced by the division of labor between agents. Running simulations in a homogeneous environment, for example, eliminated all benefits of division of labor. The simulations showed that the synergies unleashed by division of labor arise if: Economies work in a heterogeneous environment; agents engage in complementary activities whose optimization processes diverge; agents have means to synchronize their activities. This insight, although trivial if viewed a posteriori, improve our understanding of the source and nature of synergies in real economic markets and might render economic and natural sciences more consilient.
Agent based simulations visualize Adam Smith's invisible hand by solving Friedrich Hayek's Economic Calculus Klaus Jaffe
The idea of a hierarchical spatial organization of society lies at the core of seminal theories in human geography that have strongly influenced our understanding of social organization. In the same line, the recent availability of large-scale human mobility and communication data has offered novel quantitative insights hinting at a strong geographical confinement of human interactions within neighboring regions, extending to local levels within countries. However, models of human interaction largely ignore this effect. Here, we analyze several country-wide networks of telephone calls and uncover a systematic decrease of communication induced by borders which we identify as the missing variable in state-of-the-art models. Using this empirical evidence, we propose an alternative modeling framework that naturally stylize the damping effect of borders. We show that this new notion substantially improves the predictive power of widely used interaction models, thus increasing our ability to predict social activities and to plan the development of infrastructures across multiple scales.
Identifying the structural discontinuities of human interactions Sebastian Grauwin, Michael Szell, Stanislav Sobolevsky, Philipp Hövel, Filippo Simini, Maarten Vanhoof, Zbigniew Smoreda, Albert-Laszlo Barabasi, Carlo Ratti
Many real systems can be modeled as networks, where the elements of the system are nodes and interactions between elements are edges. An even larger set of systems can be modeled using dynamical processes on networks, which are in turn affected by the dynamics. Networks thus represent the backbone of many complex systems, and their theoretical and computational analysis makes it possible to gain insights into numerous applications. Networks permeate almost every conceivable discipline —including sociology, transportation, economics and finance, biology, and myriad others — and the study of “network science” has thus become a crucial component of modern scientific education. The school “Complex Networks: Theory, Methods, and Applications” offers a succinct education in network science. It is open to all aspiring scholars in any area of science or engineering who wish to study networks of any kind (whether theoretical or applied), and it is especially addressed to doctoral students and young postdoctoral scholars. The aim of the school is to deepen into both theoretical developments and applications in targeted fields.
Complex networks: theory, methods and applications Lake Como School of Advanced Studies
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