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
This article describes the themes found in the past 25 years of creativity research. Computational methods and network analysis were used to map keyword theme development across ~1,400 documents and ~5,000 unique keywords from 1990 (the first year keywords are available in Web of Science) to 2015.
Mapping the Themes, Impact, and Cohesion of Creativity Research over the Last 25 Years Rich Williams, Mark A. Runco & Eric Berlow Creativity Research Journal.Volume 28, 2016 - Issue 4 Pages 385-394 | Published online: 14 Nov 2016
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
Regarding costly punishment of two types, especially peer-punishment is considered to decrease the average payoff of all players as well as pool-punishment does, and to facilitate the antisocial punishment as a result of natural selection. To solve those problems, the author has proposed the probabilistic peer-punishment based on the difference of payoff. In the limited condition, the proposed peer-punishment has shown the positive effects on the evolution of cooperation, and increased the average payoff of all players.
Based on those findings, this study exhibits the characteristics of the evolution of cooperation by the proposed peer-punishment. Those characteristics present the significant contribution to knowledge that for the evolution of cooperation, a limited number of players should cause severe damage to defectors at the large expense of their payoff when connections between them are sparse, whereas a greater number of players should share the responsibility to punish defectors at the relatively small expense of their payoff when connections between them are dense.
Characteristics of the evolution of cooperation by the probabilistic peer-punishment based on the difference of payoff
Chaos, Solitons & Fractals Volume 95, February 2017, Pages 77–83
A characteristic property of networks is their ability to propagate influences, such as infectious diseases, behavioral changes, and failures. An especially important class of such contagious dynamics is that of cascading processes. These processes include, for example, cascading failures in infrastructure systems, extinctions cascades in ecological networks, and information cascades in social systems. In this review, we discuss recent progress and challenges associated with the modeling, prediction, detection, and control of cascades in networks.
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