Conventional wisdom holds that complex structures evolve from simpler ones, step-by-step, through a gradual evolutionary process, with Darwinian selection favoring intermediate forms along the way.
But recently some scholars have proposed that complexity can arise by other means—as a side effect, for instance—even without natural selection to promote it.
Studies suggest that random mutations that individually have no effect on an organism can fuel the emergence of complexity in a process known as constructive neutral evolution.
Economics 2.0: The Natural Step towards a Self-Regulating, Participatory Market Society
Despite all our great advances in science, technology and financial innovations, many societies today are struggling with a financial, economic and public spending crisis, over-regulation, and mass unemployment, as well as lack of sustainability and innovation. Can we still rely on conventional economic thinking or do we need a new approach? Is our economic system undergoing a fundamental transformation? Are our theories still doing a good job with just a few exceptions, or do they work only for “good weather” but not for “market storms”? Can we fix existing theories by adapting them a bit, or do we need a fundamentally different approach? These are the kind of questions that will be addressed in this paper. I argue that, as the complexity of socio-economic systems increases, networked decision-making and bottom-up self-regulation will be more and more important features. It will be explained why, besides the “homo economicus” with strictly self-regarding preferences, natural selection has also created a “homo socialis” with other-regarding preferences.(...)
Economics 2.0: The Natural Step towards a Self-Regulating, Participatory Market Society
Evolutionary and Institutional Economics Review Vol. 10 (2013) No. 1 p. 3-41
Spectral methods based on the eigenvectors of matrices are widely used in the analysis of network data, particularly for community detection and graph partitioning. Standard methods based on the adjacency matrix and related matrices, however, break down for very sparse networks, which includes many networks of practical interest. As a solution to this problem it has been recently proposed that we focus instead on the spectrum of the non-backtracking matrix, an alternative matrix representation of a network that shows better behavior in the sparse limit. Inspired by this suggestion, we here make use of a relaxation method to derive a spectral community detection algorithm that works well even in the sparse regime where other methods break down. Interestingly, however, the matrix at the heart of the method, it turns out, is not exactly the non-backtracking matrix, but a variant of it with a somewhat different definition. We study the behavior of this variant matrix for both artificial and real-world networks and find it to have desirable properties, especially in the common case of networks with broad degree distributions, for which it appears to have a better behaved spectrum and eigenvectors than the original non-backtracking matrix.
Spectral community detection in sparse networks
M. E. J. Newman
Despite their obvious relationship and overlap, the field of physics is blessed with many insightful laws, while such laws are sadly absent in biology. Here we aim to discuss how the rise of a more recent field known as synthetic biology may allow us to more directly test hypotheses regarding the possible design principles of natural biological networks and systems. In particular, this review focuses on synthetic gene regulatory networks engineered to perform specific functions or exhibit particular dynamic behaviors. Advances in synthetic biology may set the stage to uncover the relationship of potential biological principles to those developed in physics.
Synthetic biological networks
Eric Archer and Gürol M Süel 2013 Rep. Prog. Phys. 76 096602
The voter model has been studied extensively as a paradigmatic opinion dynamics' model. However, its ability for modeling real opinion dynamics has not been addressed. We introduce a noisy voter model (accounting for social influence) with agents' recurrent mobility (as a proxy for social context), where the spatial and population diversity are taken as inputs to the model. We show that the dynamics can be described as a noisy diffusive process that contains the proper anysotropic coupling topology given by population and mobility heterogeneity. The model captures statistical features of the US presidential elections as the stationary vote-share fluctuations across counties, and the long-range spatial correlations that decay logarithmically with the distance. Furthermore, it recovers the behavior of these properties when a real-space renormalization is performed by coarse-graining the geographical scale from county level through congressional districts and up to states. Finally, we analyze the role of the mobility range and the randomness in decision making which are consistent with the empirical observations.
Is the Voter Model a model for voters?
Juan Fernández-Gracia, Krzysztof Suchecki, José J. Ramasco, Maxi San Miguel, Víctor M. Eguíluz
Drawing on a large database of publicly announced R&D alliances, we track the evolution of R&D networks in a large number of economic sectors over a long time period (1986-2009). Our main goal is to evaluate temporal and sectoral robustness of the main statistical properties of empirical R&D networks. By studying a large set of indicators, we provide a more complete description of these networks with respect to the existing literature. We find that most network properties are invariant across sectors. In addition, they do not change when alliances are considered independently of the sectors to which partners belong. Moreover, we find that many properties of R&D networks are characterized by a rise-and-fall dynamics with a peak in the mid-nineties. Finally, we show that such properties of empirical R&D networks support predictions of the recent theoretical literature on R&D network formation.
The Rise and Fall of R&D Networks
Mario Vincenzo Tomasello, Mauro Napoletano, Antonios Garas, Frank Schweitzer
Using a complex system approach to address world challenges in Food and Agriculture
World food supply is crucial to the well-being of every human on the planet in the basic sense that we need food to live. It also has a profound impact on the world economy, international trade and global political stability. Furthermore, consumption of certain types and amounts foods can affect health, and the choice of livestock and plants for food production can impact sustainable use of global resources. There are communities where insufficient food causes nutritional deficiencies, and at the same time other communities eating too much food leading to obesity and accompanying diseases. These aspects reflect the utmost importance of agricultural production and conversion of commodities to food products. Moreover, all factors contributing to the food supply are interdependent, and they are an integrative part of the continuously changing, adaptive and interdependent systems in the world around us. The properties of such interdependent systems usually cannot be inferred from the properties of its parts. In addressing current challenges, like the apparent incongruences of obesity and hunger, we have to account for the complex interdependencies among areas such as physics and sociology. This is possible using the complex system approach. It encompasses an integrative multi-scale and inter-disciplinary approach. Using a complex system approach that accounts for the needs of stakeholders in the agriculture and food domain, and determines which research programs will enable these stakeholders to better anticipate emerging developments in the world around them, will enable them to determine effective intervention strategies to simultaneously optimise and safeguard their interests and the interests of the environment.
Using a complex system approach to address world challenges in Food and Agriculture
H.G.J. van Mil, E.A. Foegeding, E.J. Windhab, N. Perrot, E. van der Linden
The Social Life of Genes: Shaping Your Molecular Composition
Your DNA is not a blueprint. Day by day, week by week, your genes are in a conversation with your surroundings. Your neighbors, your family, your feelings of loneliness: They don’t just get under your skin, they get into the control rooms of your cells. Inside the new social science of genetics.
Adam Spencer: Why I fell in love with monster prime numbers
They're millions of digits long, and it takes an army of mathematicians and machines to hunt them down -- what's not to love about monster primes? Adam Spencer, comedian and lifelong math geek, shares his passion for these odd numbers, and for the mysterious magic of math.
Virality Prediction and Community Structure in Social Networks
How does network structure affect diffusion? Recent studies suggest that the answer depends on the type of contagion. Complex contagions, unlike infectious diseases (simple contagions), are affected by social reinforcement and homophily.
The role of information diffusion in the evolution of social networks
Every day millions of users are connected through online social networks, generating a rich trove of data that allows us to study the mechanisms behind human interactions. Triadic closure has been treated as the major mechanism for creating social links: if Alice follows Bob and Bob follows Charlie, Alice will follow Charlie. Here we present an analysis of longitudinal micro-blogging data, revealing a more nuanced view of the strategies employed by users when expanding their social circles. While the network structure affects the spread of information among users, the network is in turn shaped by this communication activity. This suggests a link creation mechanism whereby Alice is more likely to follow Charlie after seeing many messages by Charlie. We characterize users with a set of parameters associated with different link creation strategies, estimated by a Maximum-Likelihood approach. Triadic closure does have a strong effect on link formation, but shortcuts based on traffic are another key factor in interpreting network evolution. However, individual strategies for following other users are highly heterogeneous. Link creation behaviors can be summarized by classifying users in different categories with distinct structural and behavioral characteristics. Users who are popular, active, and influential tend to create traffic-based shortcuts, making the information diffusion process more efficient in the network.
The evolution of genetic architectures underlying quantitative traits
In the classic view introduced by R. A. Fisher, a quantitative trait is encoded by many loci with small, additive effects. Recent advances in quantitative trait loci mapping have begun to elucidate the genetic architectures underlying vast numbers of phenotypes across diverse taxa, producing observations that sometimes contrast with Fisher's blueprint.
The PyCX Project aims to develop an online repository of simple, crude, yet easy-to-understand Python sample codes for dynamic complex systems simulations, including iterative maps, cellular automata, dynamical networks and agent-based models.
Complexity in Financial Markets: Modeling Psychological Behavior in Agent-Based Models and Order Book Models (by Matthieu Cristelli)
Tools and methods from complex systems science can have a considerable impact on the way in which the quantitative assessment of economic and financial issues is approached, as discussed in this thesis. First it is shown that the self-organization of financial markets is a crucial factor in the understanding of their dynamics. In fact, using an agent-based approach, it is argued that financial markets’ stylized facts appear only in the self-organized state. Secondly, the thesis points out the potential of so-called big data science for financial market modeling, investigating how web-driven data can yield a picture of market activities: it has been found that web query volumes anticipate trade volumes. As a third achievement, the metrics developed here for country competitiveness and product complexity is groundbreaking in comparison to mainstream theories of economic growth and technological development. A key element in assessing the intangible variables determining the success of countries in the present globalized economy is represented by the diversification of the productive basket of countries. The comparison between the level of complexity of a country's productive system and economic indicators such as the GDP per capita discloses its hidden growth potential.
Handbook of Systems and Complexity in Health (edited by Joachim P Sturmberg, Carmel Martin)
This book is an introduction to health care as a complex adaptive system, a system that feeds back on itself. The first section introduces systems and complexity theory from a science, historical, epistemological, and technical perspective, describing the principles and mathematics.
Subsequent sections build on the health applications of systems science theory, from human physiology to medical decision making, population health and health services research. The aim of the book is to introduce and expand on important population health issues from a systems and complexity perspective, highlight current research developments and their implications for health care delivery, consider their ethical implications, and to suggest directions for and potential pitfalls in the future.
Applications of Chaos and Nonlinear Dynamics in Science and Engineering - Vol. 3 (edited by Santo Banerjee, Lamberto Rondoni)
Chaos and nonlinear dynamics initially developed as a new emergent field with its foundation in physics and applied mathematics. The highly generic, interdisciplinary quality of the insights gained in the last few decades has spawned myriad applications in almost all branches of science and technology—and even well beyond. Wherever quantitative modeling and analysis of complex, nonlinear phenomena is required, chaos theory and its methods can play a key role.
This third volume concentrates on reviewing further relevant contemporary applications of chaotic nonlinear systems as they apply to the various cutting-edge branches of engineering. This encompasses, but is not limited to, topics such fluctuation relations and chaotic dynamics in physics, fractals and their applications in epileptic seizures, as well as chaos synchronization.
Featuring contributions from active and leading research groups, this collection is ideal both as a reference and as a ‘recipe book’ full of tried and tested, successful engineering applications.
Spatial Simulation: Exploring Pattern and Process (by David O'Sullivan, George L. W. Perry)
Across broad areas of the environmental and social sciences, simulation models are an important way to study systems inaccessible to scientific experimental and observational methods, and also an essential complement to those more conventional approaches. The contemporary research literature is teeming with abstract simulation models whose presentation is mathematically demanding and requires a high level of knowledge of quantitative and computational methods and approaches. Furthermore, simulation models designed to represent specific systems and phenomena are often complicated, and, as a result, difficult to reconstruct from their descriptions in the literature. This book aims to provide a practical and accessible account of dynamic spatial modelling, while also equipping readers with a sound conceptual foundation in the subject, and a useful introduction to the wide-ranging literature.
Spatial Simulation: Exploring Pattern and Process is organised around the idea that a small number of spatial processes underlie the wide variety of dynamic spatial models. Its central focus on three ‘building-blocks’ of dynamic spatial models – forces of attraction and segregation, individual mobile entities, and processes of spread – guides the reader to an understanding of the basis of many of the complicated models found in the research literature. The three building block models are presented in their simplest form and are progressively elaborated and related to real world process that can be represented using them. Introductory chapters cover essential background topics, particularly the relationships between pattern, process and spatiotemporal scale. Additional chapters consider how time and space can be represented in more complicated models, and methods for the analysis and evaluation of models. Finally, the three building block models are woven together in a more elaborate example to show how a complicated model can be assembled from relatively simple components.
To aid understanding, more than 50 specific models described in the book are available online at patternandprocess.org for exploration in the freely available Netlogo platform. This book encourages readers to develop intuition for the abstract types of model that are likely to be appropriate for application in any specific context. Spatial Simulation: Exploring Pattern and Process will be of interest to undergraduate and graduate students taking courses in environmental, social, ecological and geographical disciplines. Researchers and professionals who require a non-specialist introduction will also find this book an invaluable guide to dynamic spatial simulation.
Metaphysics of Science (edited by Stephen Mumford, Matthew Tugby)
Metaphysics and Science brings together important new work within an emerging philosophical discipline: the metaphysics of science. In the opening chapter, a definition of the metaphysics of science is offered, one which explains why the topics of laws, causation, natural kinds, and emergence are at the discipline's heart. The book is then divided into four sections, which group together papers from leading academics on each of those four topics. Among the questions discussed are: How are laws and measurement methods related? Can a satisfactory reductive account of laws be given? How can Lorentz transformation laws be explained? How are dispositions triggered? What role should dispositional properties play in our understanding of causation? Are natural kinds and natural properties distinct? How is the Kripke-Putnam semantics for natural kind terms related to the natural kind essentialist thesis? What would have to be the case for natural kind terms to have determinate reference? What bearing, if any, does nonlinearity in science have on the issue of metaphysical emergence? This collection will be of interest to philosophers, scientists and post-graduates working on problems at the intersection of metaphysics and science.
The Cancer Chronicles: Unlocking Medicine's Deepest Mystery (by George Johnson)
When the woman he loved was diagnosed with a metastatic cancer, science writer George Johnson embarked on a journey to learn everything he could about the disease and the people who dedicate their lives to understanding and combating it. What he discovered is a revolution under way—an explosion of new ideas about what cancer really is and where it comes from. In a provocative and intellectually vibrant exploration, he takes us on an adventure through the history and recent advances of cancer research that will challenge everything you thought you knew about the disease.
Deftly excavating and illuminating decades of investigation and analysis, he reveals what we know and don’t know about cancer, showing why a cure remains such a slippery concept. We follow him as he combs through the realms of epidemiology, clinical trials, laboratory experiments, and scientific hypotheses—rooted in every discipline from evolutionary biology to game theory and physics. Cogently extracting fact from a towering canon of myth and hype, he describes tumors that evolve like alien creatures inside the body, paleo-oncologists who uncover petrified tumors clinging to the skeletons of dinosaurs and ancient human ancestors, and the surprising reversals in science’s comprehension of the causes of cancer, with the foods we eat and environmental toxins playing a lesser role. Perhaps most fascinating of all is how cancer borrows natural processes involved in the healing of a wound or the unfolding of a human embryo and turns them, jujitsu-like, against the body.
Throughout his pursuit, Johnson clarifies the human experience of cancer with elegiac grace, bearing witness to the punishing gauntlet of consultations, surgeries, targeted therapies, and other treatments. He finds compassion, solace, and community among a vast network of patients and professionals committed to the fight and wrestles to comprehend the cruel randomness cancer metes out in his own family. For anyone whose life has been affected by cancer and has found themselves asking why?, this book provides a new understanding. In good company with the works of Atul Gawande, Siddhartha Mukherjee, and Abraham Verghese, The Cancer Chronicles is endlessly surprising and as radiant in its prose as it is authoritative in its eye-opening science.
Choice Modelling: The State of the Art and the State of Practice (edited by Stephane Hess, Andrew Daly)
Choice modelling has been one of the most active fields in economics over recent years. This valuable new book contains leading contributions from academics and practitioners from across the different areas of study where choice modelling is a key analytical technique, drawn from a recent international conference.
Choice models explain the behavior of individuals by quantifying their values, responses and perceptions of attributes describing the various options (alternatives) available to them. Policy makers and planners have long since recognised the potential of using choice models for guidance purposes, with applications in fields as diverse as transport analysis, healthcare, telecommunications, public service evaluation and energy.
The unique mix of theoretical and applied chapters will appeal to academics, students, researchers and practitioners in various fields, as well as anyone with a general interest in the subject.
Feeling Beauty: The Neuroscience of Aesthetic Experience (by G. Gabrielle Starr)
In Feeling Beauty, G. Gabrielle Starr argues that understanding the neural underpinnings of aesthetic experience can reshape our conceptions of aesthetics and the arts. Drawing on the tools of both cognitive neuroscience and traditional humanist inquiry, Starr shows that neuroaesthetics offers a new model for understanding the dynamic and changing features of aesthetic life, the relationships among the arts, and how individual differences in aesthetic judgment shape the varieties of aesthetic experience.
Starr, a scholar of the humanities and a researcher in the neuroscience of aesthetics, proposes that aesthetic experience relies on a distributed neural architecture -- a set of brain areas involved in emotion, perception, imagery, memory, and language. More important, it emerges from networked interactions, intricately connected and coordinated brain systems that together form a flexible architecture enabling us to develop new arts and to see the world around us differently. Focusing on the "sister arts" of poetry, painting, and music, Starr builds and tests a neural model of aesthetic experience valid across all the arts. Asking why works that address different senses using different means seem