In this story, a dog introduces us to a strange creature that burrows beneath forests, building an underground network where deals are made and lives are saved (and lost) in a complex web of friendships, rivalries, and business relations. It’s a network that scientists are only just beginning to untangle and map, and it’s not only turning our understanding of forests upside down, it’s leading some researchers to rethink what it means to be intelligent.
The bitterly factious 2016 U.S. presidential election campaign was the culmination of several trends that, taken together, constitute a syndrome of chronic ailments in the body politic. Ironically, these destructive trends have accelerated just as science has rapidly improved our understanding of them and their underlying causes. But mere understanding is not sufficient to repair our politics. The challenge is to build a translational science of democracy that maintains scientific rigor while actively promoting the health of the body politic.
Michael A. Neblo, William Minozzi, Kevin M. Esterling, Jon Green, Jonathon Kingzette, David M. J. Lazer
Science 03 Mar 2017: Vol. 355, Issue 6328, pp. 914-915 DOI: 10.1126/science.aal3900
Community detection, the decomposition of a graph into essential building blocks, has been a core research topic in network science over the past years. Since a precise notion of what constitutes a community has remained evasive, community detection algorithms have often been compared on benchmark graphs with a particular form of assortative community structure and classified based on the mathematical techniques they employ. However, this comparison can be misleading because apparent similarities in their mathematical machinery can disguise different goals and reasons for why we want to employ community detection in the first place. Here we provide a focused review of these different motivations that underpin community detection. This problem-driven classification is useful in applied network science, where it is important to select an appropriate algorithm for the given purpose. Moreover, highlighting the different facets of community detection also delineates the many lines of research and points out open directions and avenues for future research.
The many facets of community detection in complex networks Michael T. SchaubEmail author, Jean-Charles Delvenne, Martin Rosvall and Renaud Lambiotte Applied Network Science20172:4 DOI: 10.1007/s41109-017-0023-6
David Edery, who was until recently part of the CMS staff and now works for Microsoft, has been generating some interesting discussion over on his blog, Game Tycoon, about how games might harness “the wisdom of crowds” to solve real world problems. It’s an idea he’s been promoting for some time but I only recently had a chance to read through all of his discussion. He starts by describing the growing academic interest that has been generated by James Surowiecki’s The Wisdom of Crowds and then suggesting some of the challenges of applying these concepts in a real world context: -
Once considered provocative, the notion that the wisdom of the crowd is superior to any individual has become itself a piece of crowd wisdom, leading to speculation that online voting may soon put credentialed experts out of business. Recent applications include political and economic forecasting, evaluating nuclear safety, public policy, the quality of chemical probes, and possible responses to a restless volcano. Algorithms for extracting wisdom from the crowd are typically based on a democratic voting procedure. They are simple to apply and preserve the independence of personal judgment. However, democratic methods have serious limitations. They are biased for shallow, lowest common denominator information, at the expense of novel or specialized knowledge that is not widely shared. Adjustments based on measuring confidence do not solve this problem reliably. Here we propose the following alternative to a democratic vote: select the answer that is more popular than people predict. We show that this principle yields the best answer under reasonable assumptions about voter behaviour, while the standard ‘most popular’ or ‘most confident’ principles fail under exactly those same assumptions. Like traditional voting, the principle accepts unique problems, such as panel decisions about scientific or artistic merit, and legal or historical disputes. The potential application domain is thus broader than that covered by machine learning and psychometric methods, which require data across multiple questions.
A solution to the single-question crowd wisdom problem
Dražen Prelec, H. Sebastian Seung & John McCoy
Nature 541, 532–535 (26 January 2017) doi:10.1038/nature21054
The International Conference on Complex Networks and their Applications aims at bringing together researchers from different scientific communities working on areas related to complex networks.
Two types of contributions are welcome: theoretical developments arising from practical problems, and case studies where methodologies are applied. Both contributions are aimed at stimulating the interaction between theoreticians and practitioners.
The 6th International Conference on Complex Networks and Their Applications November 29 - December 01 2017 Lyon, France
Face-to-face social interactions enhance well-being. With the ubiquity of social media, important questions have arisen about the impact of online social interactions. In the present study, we assessed the associations of both online and offline social networks with several subjective measures of well-being. We used 3 waves (2013, 2014, and 2015) of data from 5,208 subjects in the nationally representative Gallup Panel Social Network Study survey, including social network measures, in combination with objective measures of Facebook use. We investigated the associations of Facebook activity and real-world social network activity with self-reported physical health, self-reported mental health, self-reported life satisfaction, and body mass index. Our results showed that overall, the use of Facebook was negatively associated with well-being. For example, a 1-standard-deviation increase in "likes clicked" (clicking "like" on someone else's content), "links clicked" (clicking a link to another site or article), or "status updates" (updating one's own Facebook status) was associated with a decrease of 5%-8% of a standard deviation in self-reported mental health. These associations were robust to multivariate cross-sectional analyses, as well as to 2-wave prospective analyses. The negative associations of Facebook use were comparable to or greater in magnitude than the positive impact of offline interactions, which suggests a possible tradeoff between offline and online relationships.
Association of Facebook Use With Compromised Well-Being: A Longitudinal Study. Shakya HB, Christakis NA. Am J Epidemiol. 2017 Jan 16. doi: 10.1093/aje/kww189
Transformation involves fundamental change, which in the context of sustainability, requires radical, systemic shifts in values and beliefs, patterns of social behavior, and multilevel governance and management regimes (Olsson et al. 2014).
"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.
How can systems in which individuals’ inner workings are very similar to each other, as neural networks or ant colonies, produce so many qualitatively different behaviors, giving rise to roles and specialization? In this work, we bring new perspectives to this question by focusing on the underlying network that defines how individuals in these systems interact. We applied a genetic algorithm to optimize rules and connections of cellular automata in order to solve the density classification task, a classical problem used to study emergent behaviors in decentralized computational systems. The networks used were all generated by the introduction of shortcuts in an originally regular topology, following the small-world model. Even though all cells follow the exact same rules, we observed the existence of different classes of cells’ behaviors in the best cellular automata found—most cells were responsible for memory and others for integration of information. Through the analysis of structural measures and patterns of connections (motifs) in successful cellular automata, we observed that the distribution of shortcuts between distant regions and the speed in which a cell can gather information from different parts of the system seem to be the main factors for the specialization we observed, demonstrating how heterogeneity in a network can create heterogeneity of behavior.
Godoy A, Tabacof P, Von Zuben FJ (2017) The role of the interaction network in the emergence of diversity of behavior. PLoS ONE 12(2): e0172073. doi:10.1371/journal.pone.0172073
Organizations create networks with one another, and these networks may in turn shape the organizations involved. Until recently, such complex dynamic processes could not be rigorously empirically analyzed because of a lack of suitable modeling and validation methods. Using stochastic actor-oriented models and unique longitudinal survey data on the changing structure of interfirm production networks in the automotive industry in Japan, this paper illustrates how to quantitatively assess and validate (1) the dynamic micro-mechanism by which organizations form their networks and (2) the role of the dynamic network structures in organizational performance. The applied model helps to explain the endogenous processes behind the recent diversification of Japanese automobile production networks. Specifically, testing the effects of network topology and network diffusion on organizational performance, the novel modeling framework enables us to discern that the restructuring of interorganizational networks led to the increase of Japanese automakers’ production per employee, and not the reverse. Traditional models that do not allow for interaction between interorganizational structure and organizational agency misrepresent this mechanism.
Analyzing the coevolution of interorganizational networks and organizational performance: Automakers’ production networks in Japan
Matous, P. & Todo, Y. Appl Netw Sci (2017) 2: 5. doi:10.1007/s41109-017-0024-5
I am a big fan of A16Z podcasts, and they just released a terrific episode on Network Effect. Network effect is an important, and somewhat confusing topic. The reason network effect is important is that the businesses with a true network effect are highly defensible, have strong retention and engagement, exhibit characteristics of a monopoly, and tend to last for…
Many adaptive systems sit near a tipping or critical point. For systems near a critical point small changes to component behaviour can induce large-scale changes in aggregate structure and function. Criticality can be adaptive when the environment is changing, but entails reduced robustness through sensitivity. This tradeoff can be resolved when criticality can be tuned. We address the control of finite measures of criticality using data on fight sizes from an animal society model system (Macaca nemestrina, n=48). We find that a heterogeneous, socially organized system, like homogeneous, spatial systems (flocks and schools), sits near a critical point; the contributions individuals make to collective phenomena can be quantified; there is heterogeneity in these contributions; and distance from the critical point (DFC) can be controlled through biologically plausible mechanisms exploiting heterogeneity. We propose two alternative hypotheses for why a system decreases the distance from the critical point.
Control of finite critical behaviour in a small-scale social system Bryan C. Daniels, David C. Krakauer & Jessica C. Flack Nature Communications 8, Article number: 14301 (2017) doi:10.1038/ncomms14301
Based on interviews with survivors and a review of the data, we believe that communities with more ties, interaction, and shared norms worked effectively to provide help to kin, family, and neighbors. In many cases only 40 minutes separated the earthquake and the arrival of the tsunami. During that time, residents literally picked up and carried many elderly people out of vulnerable, low-lying areas. In high-trust neighborhoods, people knocked on doors of those who needed help and escorted them out of harm’s way.
Coping with the complexities of the social world in the 21st century requires deeper quantitative and predictive understanding. Forty-three internationally acclaimed scientists and thinkers share their vision for complexity science in the next decade in this invaluable book. Topics cover how complexity and big data science could help society to tackle the great challenges ahead, and how the newly established Complexity Science Hub Vienna might be a facilitator on this path.
43 Visions for Complexity. Edited by Stefan Thurner
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