For our vision, The Institute for Research in Complexity and Society is dedicated to applying insights from the study of complexity science to social problems faced by the world community. What is unique about our institute is that we address these issues in a manner that is rigorous theoretically, is supported empirically, and applied pragmatically. That is why we bring together scientists, mathematicians, practitioners, organizational leaders, and technological pioneers in a collaborative setting unhampered by the downsides of traditional academic and government organizational cultures.
Our mission is to engage with communities of practice, small and large organizations, and social enterprises directly, and to do so in ways that improve their effectiveness and at the same time further the development of theory and research methods generalizable to in other milieus as a means to further the accumulation and measurement of social value.
When small companies grow rapidly, the culture can get lost in a sea of new people, processes, geographic expansion, aggressive growth targets, and the avalanche of changes needed to scale. The culture can become a boat anchor, dragging behind the desired direction and pulling people in the wrong direction. But when senior leaders make a conscious decision to keep the best of the cultural elements that brought the company success in the first place, great things can happen.
Cafe Rio Mexican Grill did just that. In 2011, Dave Gagnon, a former Burger King senior vice president of North America company operations and training, took over as CEO and COO. Andy Hooper, who had led the culture-shaping work at Burger King, joined Cafe Rio as chief people officer. The organization had an outstanding culture, and was in its third year of nearly double-digit comparable sales growth. But to grow rapidly, the executive leadership team needed to codify the culture that was largely built on ‘tribal knowledge transfer’ to scale for national expansion.
We study the dynamic network of real world person-to-person interactions between approximately 1,000 individuals with 5-min resolution across several months. There is currently no coherent theoretical framework for summarizing the tens of thousands of interactions per day in this complex network, but here we show that at the right temporal resolution, social groups can be identified directly. We outline and validate a framework that enables us to study the statistical properties of individual social events as well as series of meetings across weeks and months. Representing the dynamic network as sequences of such meetings reduces the complexity of the system dramatically. We illustrate the usefulness of the framework by investigating the predictability of human social activity.
Fundamental structures of dynamic social networks Vedran Sekara, Arkadiusz Stopczynski, and Sune Lehmann
“Big Bet” philanthropy has gotten a lot of press lately, and, indeed, the dollar amounts here are somewhat staggering in the nonprofit world, but the Blue Meridian initiative has some special design aspects that deserve a thoughtful response from readers.
With big data, we can multiply our options and filter out things we don’t want to see. But there is much to be said for making discoveries through pure serendipity: contingency and randomness often furnish the transformational or counterintuitive ideas that propel humanity forward.
The living-together of distinct organisms in a single termite nest along with the termite builder colony, is emblematic in its ecological and evolutionary significance. On top of preserving biodiversity, these interspecific and intraspecific symbioses provide useful examples of interindividual associations thought to underly transitions in organic evolution. Being interindividual in nature, such processes may involve emergent phenomena and hence call for analytical solutions provided by computing tools and modelling, as opposed to classical biological methods of analysis. Here we provide selected examples of such solutions, showing that termite studies may profit from a symbiotic-like link with computing science to open up wide and new research avenues in ecology and evolution.
Fruitful symbioses between termites and computers Og DeSouza, Elio Tuci, Octavio Miramontes
In the last years, network scientists have directed their interest to the multi-layer character of real-world systems, and explicitly considered the structural and dynamical organization of graphs made of diverse layers between its constituents. Most complex systems include multiple subsystems and layers of connectivity and, in many cases, the interdependent components of systems interact through many different channels. Such a new perspective is indeed found to be the adequate representation for a wealth of features exhibited by networked systems in the real world. The contributions presented in this Focus Issue cover, from different points of view, the many achievements and still open questions in the field of multi-layer networks, such as: new frameworks and structures to represent and analyze heterogeneous complex systems, different aspects related to synchronization and centrality of complex networks, interplay between layers, and applications to logistic, biological, social, and technological fields.
Introduction to Focus Issue: Complex Dynamics in Networks, Multilayered Structures and Systems Stefano Boccaletti, Regino Criado, Miguel Romance and Joaquín J. Torres
Our innovation system has terribly failed. It is well designed to support gradual improvements of our knowledge and technologies. But it does not support disruptive innovations well, which would create new qualities and functionalities, or question the basis of our established knowledge and routines. Moreover, our knowledge does not keep up anymore with the pace at which our world changes, and solutions to new problems often come with serious delays. Therefore, we need to re-invent innovation. In particularly, we must learn to create systems embracing collective intelligence that surpasses the intelligence of even the brightest individual and of powerful supercomputing solutions. This cannot be based on top-down nor majority decisions. Diversity is absolutely crucial for collective intelligence to work…
WHY OUR INNOVATION SYSTEM IS FAILING - and How to Change This
Networks are a fundamental tool for understanding and modeling complex systems in physics, biology, neuroscience, engineering, and social science. Many networks are known to exhibit rich, lower-order connectivity patterns that can be captured at the level of individual nodes and edges. However, higher-order organization of complex networks—at the level of small network subgraphs—remains largely unknown. Here, we develop a generalized framework for clustering networks on the basis of higher-order connectivity patterns. This framework provides mathematical guarantees on the optimality of obtained clusters and scales to networks with billions of edges. The framework reveals higher-order organization in a number of networks, including information propagation units in neuronal networks and hub structure in transportation networks. Results show that networks exhibit rich higher-order organizational structures that are exposed by clustering based on higher-order connectivity patterns.
Higher-order organization of complex networks Austin R. Benson, David F. Gleich, Jure Leskovec
Complex network growth across diverse fields of science is hypothesized to be driven in the main by a combination of preferential attachment and node fitness processes. For measuring the respective influences of these processes, previous approaches make strong and untested assumptions on the functional forms of either the preferential attachment function or fitness function or both. We introduce a Bayesian statistical method called PAFit to estimate preferential attachment and node fitness without imposing such functional constraints that works by maximizing a log-likelihood function with suitably added regularization terms. We use PAFit to investigate the interplay between preferential attachment and node fitness processes in a Facebook wall-post network. While we uncover evidence for both preferential attachment and node fitness, thus validating the hypothesis that these processes together drive complex network evolution, we also find that node fitness plays the bigger role in determining the degree of a node. This is the first validation of its kind on real-world network data. But surprisingly the rate of preferential attachment is found to deviate from the conventional log-linear form when node fitness is taken into account. The proposed method is implemented in the R package PAFit.
Joint estimation of preferential attachment and node fitness in growing complex networks Thong Pham, Paul Sheridan & Hidetoshi Shimodaira Scientific Reports 6, Article number: 32558 (2016) doi:10.1038/srep32558
Spontaneous synchronization has long served as a paradigm for behavioral uniformity that can emerge from interactions in complex systems. When the interacting entities are identical and their coupling patterns are also identical, the complete synchronization of the entire network is the state inheriting the system symmetry. As in other systems subject to symmetry breaking, such symmetric states are not always stable. Here, we report on the discovery of the converse of symmetry breaking—the scenario in which complete synchronization is not stable for identically coupled identical oscillators but becomes stable when, and only when, the oscillator parameters are judiciously tuned to nonidentical values, thereby breaking the system symmetry to preserve the state symmetry. Aside from demonstrating that diversity can facilitate and even be required for uniformity and consensus, this suggests a mechanism for convergent forms of pattern formation in which initially asymmetric patterns evolve into symmetric ones.
Symmetric States Requiring System Asymmetry Takashi Nishikawa and Adilson E. Motter Phys. Rev. Lett. 117, 114101
Hierarchy is a ubiquitous organizing principle in biology, and a key reason evolution produces complex, evolvable organisms, yet its origins are poorly understood. Here we demonstrate for the first time that hierarchy evolves as a result of the costs of network connections. We confirm a previous finding that connection costs drive the evolution of modularity, and show that they also cause the evolution of hierarchy. We further confirm that hierarchy promotes evolvability in addition to evolvability caused by modularity. Because many biological and human-made phenomena can be represented as networks, and because hierarchy is a critical network property, this finding is immediately relevant to a wide array of fields, from biology, sociology, and medical research to harnessing evolution for engineering.
Mengistu H, Huizinga J, Mouret J-B, Clune J (2016) The Evolutionary Origins of Hierarchy. PLoS Comput Biol 12(6): e1004829. doi:10.1371/journal.pcbi.1004829
Why did the New York Stock Exchange suspend trading without warning on July 8, 2015? Why did certain Toyota vehicles accelerate uncontrollably against the will of their drivers? Why does the programming inside our airplanes occasionally surprise its creators? After a thorough analysis by the top experts, the answers still elude us. You don’t understand the software running your car or your iPhone. But here’s a secret: neither do the geniuses at Apple or the Ph.D.’s at Toyota—not perfectly, anyway. No one, not lawyers, doctors, accountants, or policy makers, fully grasps the rules governing your tax return, your retirement account, or your hospital’s medical machinery. The same technological advances that have simplified our lives have made the systems governing our lives incomprehensible, unpredictable, and overcomplicated. In Overcomplicated, complexity scientist Samuel Arbesman offers a fresh, insightful field guide to living with complex technologies that defy human comprehension. As technology grows more complex, Arbesman argues, its behavior mimics the vagaries of the natural world more than it conforms to a mathematical model. If we are to survive and thrive in this new age, we must abandon our need for governing principles and rules and accept the chaos. By embracing and observing the freak accidents and flukes that disrupt our lives, we can gain valuable clues about how our algorithms really work. What’s more, we will become better thinkers, scientists, and innovators as a result.
What do societies, the Internet, and the human brain have in common? The immediate answer might be "not that much", but in reality they are all examples of complex relational systems, whose emerging behaviours are largely determined by the non-trivial networks of interactions among their constituents, namely individuals, computers, or neurons. In the last two decades, network scientists have proposed models of increasing complexity to better understand real-world systems. Only recently we have realised that multiplexity, i.e. the coexistence of several types of interactions among the constituents of a complex system, is responsible for substantial qualitative and quantitative differences in the type and variety of behaviours that a complex system can exhibit. As a consequence, multilayer and multiplex networks have become a hot topic in complexity science. Here we provide an overview of some of the measures proposed so far to characterise the structure of multiplex networks, and a selection of models aiming at reproducing those structural properties and at quantifying their statistical significance. Focusing on a subset of relevant topics, this brief review is a quite comprehensive introduction to the most basic tools for the analysis of multiplex networks observed in the real-world. The wide applicability of multiplex networks as a framework to model complex systems in different fields, from biology to social sciences, and the colloquial tone of the paper will make it an interesting read for researchers working on both theoretical and experimental analysis of networked systems.
The new challenges of multiplex networks: measures and models Federico Battiston, Vincenzo Nicosia, Vito Latora
Not many startups have spent a decade fine-tuning their tech platform prior to launch. But not many startups are trying to radically rethink the structure of the internet.
UK-based MaidSafe, which has been building an alternative, decentralized p2p network since before Steve Jobs announced the original iPhone, is finally — finally! — gearing up for a tentative launch — flicking the switch on its first alpha test network today.
"A forest is much more than what you see," says ecologist Suzanne Simard. Her 30 years of research in Canadian forests have led to an astounding discovery -- trees talk, often and over vast distances. Learn more about the harmonious yet complicated social lives of trees and prepare to see the natural world with new eyes.
Online social systems are multiplex in nature as multiple links may exist between the same two users across different social media. In this work, we study the geo-social properties of multiplex links, spanning more than one social network and apply their structural and interaction features to the problem of link prediction across social networking services. Exploring the intersection of two popular online platforms - Twitter and location-based social network Foursquare - we represent the two together as a composite multilayer online social network, where each platform represents a layer in the network. We find that pairs of users connected on both services, have greater neighbourhood similarity and are more similar in terms of their social and spatial properties on both platforms in comparison with pairs who are connected on just one of the social networks. Our evaluation, which aims to shed light on the implications of multiplexity for the link generation process, shows that we can successfully predict links across social networking services. In addition, we also show how combining information from multiple heterogeneous networks in a multilayer configuration can provide new insights into user interactions on online social networks, and can significantly improve link prediction systems with valuable applications to social bootstrapping and friend recommendations.
A multilayer approach to multiplexity and link prediction in online geo-social networks Hristova D, Noulas A, Brown C, Musolesi M, Mascolo C EPJ Data Science 2016, 5 :24 (26 July 2016)
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