Evolutionary games on structured populations have been studied extensively in recent years. In reality, social interactions take place in different domains, which naturally requires a multiplex description. The impact of the multiplex nature of human interactions on the evolution of cooperation has recently attracted a lot of attention, however, the fundamental mechanisms at play are still not well understood. Here, we show that the interplay between the structural organization of the multiplex and the assumptions about the dynamical coupling between the layers leads to very different outcomes. We show that the organization of the multiplex can enable mutual spatial selection, which refers to the formation of overlapping clusters of cooperators in different layers that can survive in social dilemmas. Furthermore, heterogeneity and degree correlations lead to topological enslavement, which means that the hubs dominate the game dynamics inducing payoff irrelevance. Our findings reveal the fundamental mechanisms at play and provide a new perspective for understanding the evolution of cooperation on realistic structured populations.

 

Evolutionary games on scale-free multiplex networks
Kaj-Kolja Kleineberg

Source: arxiv.org

World record setting has long attracted public interest and scientific investigation. Extremal records summarize the limits of the space explored by a process, and the historical progression of a record sheds light on the underlying dynamics of the process. Existing analyses of prediction, statistical properties, and ultimate limits of record progressions have focused on particular domains. However, a broad perspective on how record progressions vary across different spheres of activity needs further development. Here we employ cross-cutting metrics to compare records across a variety of domains, including sports, games, biological evolution, and technological development. We find that these domains exhibit characteristic statistical signatures in terms of rates of improvement, “burstiness” of record-breaking time series, and the acceleration of the record breaking process. Specifically, sports and games exhibit the slowest rate of improvement and a wide range of rates of “burstiness.” Technology improves at a much faster rate and, unlike other domains, tends to show acceleration in records. Many biological and technological processes are characterized by constant rates of improvement, showing less burstiness than sports and games. It is important to understand how these statistical properties of record progression emerge from the underlying dynamics. Towards this end, we conduct a detailed analysis of a particular record-setting event: elite marathon running. In this domain, we find that studying record-setting data alone can obscure many of the structural properties of the underlying process. The marathon study also illustrates how some of the standard statistical assumptions underlying record progression models may be inappropriate or commonly violated in real-world datasets.

 

On the records

Andrew Berdahl, Uttam Bhat, Vanessa Ferdinand, Joshua Garland, Keyan Ghazi-Zahedi, Justin Grana, Joshua A. Grochow, Elizabeth Hobson, Yoav Kallus, Christopher P. Kempes, Artemy Kolchinsky, Daniel B. Larremore, Eric Libby, Eleanor A. Power, Brendan D. Tracey (Santa Fe Institute Postdocs)

Source: arxiv.org

What is the potential of machine learning over the next 5-10 years? And how can we develop this technology in a way that benefits everyone? 

The Royal Society’s machine learning project has been investigating these questions, and has today launched a report setting out the action needed to maintain the UK’s role in advancing this technology while ensuring careful stewardship of its development.

Machine learning is a form of artificial intelligence that allows computer systems to learn from examples, data, and experience. Through enabling computers to perform specific tasks intelligently, machine learning systems can carry out complex processes by learning from data, rather than following pre-programmed rules.

Source: royalsociety.org

Science helps us think more clearly about migration, in part by showing its deep roots. Researchers wielding powerful new methods have discovered ancient, hidden migrations that shaped today’s populations. Go back far enough and almost all of us are immigrants, despite cherished stories of ethnic and national origins. Science can also aid the 21 million migrants today who are refugees from violence or famine, according to the United Nations. They need food, medicine, and shelter now, but in the long run it is their mental health that will be key to building new lives, as shown by a case study of the long-persecuted Yezidis. The success of these and other immigrants depends in part on whether new countries spurn or welcome them, and research is starting to show how to manage our long-standing biases against outsiders.

 

People on the move
Elizabeth Culotta

Science 19 May 2017:
Vol. 356, Issue 6339, pp. 676-677
DOI: 10.1126/science.356.6339.676

Source: science.sciencemag.org

Coordination in groups faces a sub-optimization problem and theory suggests that some randomness may help to achieve global optima. Here we performed experiments involving a networked colour coordination game in which groups of humans interacted with autonomous software agents (known as bots). Subjects (n = 4,000) were embedded in networks (n = 230) of 20 nodes, to which we sometimes added 3 bots. The bots were programmed with varying levels of behavioural randomness and different geodesic locations. We show that bots acting with small levels of random noise and placed in central locations meaningfully improve the collective performance of human groups, accelerating the median solution time by 55.6%. This is especially the case when the coordination problem is hard. Behavioural randomness worked not only by making the task of humans to whom the bots were connected easier, but also by affecting the gameplay of the humans among themselves and hence creating further cascades of benefit in global coordination in these heterogeneous systems.

 

Locally noisy autonomous agents improve global human coordination in network experiments

Hirokazu Shirado & Nicholas A. Christakis

Nature 545, 370–374 (18 May 2017) doi:10.1038/nature22332

Source: www.nature.com