🔹 Teaching epidemiologists to code.
http://www.episkills.com/
http://www.episkills.com/
⚔️ 💵 ♦️Game Theory I - Static Games
Lead instructor: Justin Grana
🔗 https://www.complexityexplorer.org/tutorials/69-game-theory-i-static-games
⚡️ About the Tutorial:
Game theory is the standard quantitative tool for analyzing the interactions of multiple decision makers. Its applications extend to economics, biology, engineering and even cyber security. Furthermore, many complex systems involve multiple decision makers and thus a full analysis of such systems necessitates the tools of game theory. This course is designed to provide a high-level introduction to static, non-cooperative game theory. The main goal of this course is to introduce students to the idea of a Nash Equilibrium and how the Nash Equilibrium solution concept can be applied to a number of scenarios. Students are assumed to be familiar with the concept of expected value and the basics of probability. While calculus is not required for the majority of the course, lesson 7 focuses on an example that employs calculus. However, lesson 7 can be skipped without any harm in understanding lessons 8 − 10.
Lead instructor: Justin Grana
🔗 https://www.complexityexplorer.org/tutorials/69-game-theory-i-static-games
⚡️ About the Tutorial:
Game theory is the standard quantitative tool for analyzing the interactions of multiple decision makers. Its applications extend to economics, biology, engineering and even cyber security. Furthermore, many complex systems involve multiple decision makers and thus a full analysis of such systems necessitates the tools of game theory. This course is designed to provide a high-level introduction to static, non-cooperative game theory. The main goal of this course is to introduce students to the idea of a Nash Equilibrium and how the Nash Equilibrium solution concept can be applied to a number of scenarios. Students are assumed to be familiar with the concept of expected value and the basics of probability. While calculus is not required for the majority of the course, lesson 7 focuses on an example that employs calculus. However, lesson 7 can be skipped without any harm in understanding lessons 8 − 10.
🔹 Pulsing to their own beat: What's cuing salmon migration patterns? | Santa Fe Institute
https://www.santafe.edu/news-center/news/pulsing-their-own-beat-whats-cuing-salmon-migration-patterns
https://www.santafe.edu/news-center/news/pulsing-their-own-beat-whats-cuing-salmon-migration-patterns
www.santafe.edu
Pulsing to their own beat: What's cuing salmon migration patterns? | Santa Fe Institute
🔹 Fractals used to estimate depth of all the world's lakes.
http://www.sciencemag.org/news/2017/03/world-s-lakes-are-much-shallower-thought-mathematical-analysis-suggests?utm_source=newsfromscience&utm_medium=twitter&utm_campaign=deeplake-11848
http://www.sciencemag.org/news/2017/03/world-s-lakes-are-much-shallower-thought-mathematical-analysis-suggests?utm_source=newsfromscience&utm_medium=twitter&utm_campaign=deeplake-11848
Science | AAAS
World’s lakes are much shallower than thought, mathematical analysis suggests
If true, lakes produce more heat-trapping methane than previously estimated
SICC - Italian Society for Chaos and Complexity http://www.sicc-it.org/en/
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Rank diversity: Learning how teams and players stack up and why
🗞 The Role of Network Analysis in Industrial and Applied Mathematics
Mason A. Porter, Sam D. Howison
🔗 https://arxiv.org/pdf/1703.06843
📌 ABSTRACT
Many problems in industry --- and in the social, natural, information, and medical sciences --- involve discrete data and benefit from approaches from subjects such as network science, information theory, optimization, probability, and statistics. Because the study of networks is concerned explicitly with connectivity between different entities, it has become very prominent in industrial settings, and this importance has been accentuated further amidst the modern data deluge. In this article, we discuss the role of network analysis in industrial and applied mathematics, and we give several examples of network science in industry.
Mason A. Porter, Sam D. Howison
🔗 https://arxiv.org/pdf/1703.06843
📌 ABSTRACT
Many problems in industry --- and in the social, natural, information, and medical sciences --- involve discrete data and benefit from approaches from subjects such as network science, information theory, optimization, probability, and statistics. Because the study of networks is concerned explicitly with connectivity between different entities, it has become very prominent in industrial settings, and this importance has been accentuated further amidst the modern data deluge. In this article, we discuss the role of network analysis in industrial and applied mathematics, and we give several examples of network science in industry.
Which friends are more popular than you? Contact strength and the friendship paradox in social networks
James P. Bagrow, Christopher M. Danforth, Lewis Mitchell
🔗 https://arxiv.org/pdf/1703.06361
📌 ABSTRACT
The friendship paradox states that in a social network, egos tend to have lower degree than their alters, or, "your friends have more friends than you do". Most research has focused on the friendship paradox and its implications for information transmission, but treating the network as static and unweighted. Yet, people can dedicate only a finite fraction of their attention budget to each social interaction: a high-degree individual may have less time to dedicate to individual social links, forcing them to modulate the quantities of contact made to their different social ties. Here we study the friendship paradox in the context of differing contact volumes between egos and alters, finding a connection between contact volume and the strength of the friendship paradox. The most frequently contacted alters exhibit a less pronounced friendship paradox compared with the ego, whereas less-frequently contacted alters are more likely to be high degree and give rise to the paradox. We argue therefore for a more nuanced version of the friendship paradox: "your closest friends have slightly more friends than you do", and in certain networks even: "your best friend has no more friends than you do". We demonstrate that this relationship is robust, holding in both a social media and a mobile phone dataset. These results have implications for information transfer and influence in social networks, which we explore using a simple dynamical model.
James P. Bagrow, Christopher M. Danforth, Lewis Mitchell
🔗 https://arxiv.org/pdf/1703.06361
📌 ABSTRACT
The friendship paradox states that in a social network, egos tend to have lower degree than their alters, or, "your friends have more friends than you do". Most research has focused on the friendship paradox and its implications for information transmission, but treating the network as static and unweighted. Yet, people can dedicate only a finite fraction of their attention budget to each social interaction: a high-degree individual may have less time to dedicate to individual social links, forcing them to modulate the quantities of contact made to their different social ties. Here we study the friendship paradox in the context of differing contact volumes between egos and alters, finding a connection between contact volume and the strength of the friendship paradox. The most frequently contacted alters exhibit a less pronounced friendship paradox compared with the ego, whereas less-frequently contacted alters are more likely to be high degree and give rise to the paradox. We argue therefore for a more nuanced version of the friendship paradox: "your closest friends have slightly more friends than you do", and in certain networks even: "your best friend has no more friends than you do". We demonstrate that this relationship is robust, holding in both a social media and a mobile phone dataset. These results have implications for information transfer and influence in social networks, which we explore using a simple dynamical model.
1703.07317.pdf
6.2 MB
Must read paper on the predictability of #epidemics:
🔹 On the predictability of infectious disease outbreaks
Samuel V. Scarpino and Giovanni Petri
🔹 On the predictability of infectious disease outbreaks
Samuel V. Scarpino and Giovanni Petri
🔸 Do we need to age? How does altruism arise? How do new species form? Explore the basics of evolutionary dynamics:
http://www.necsi.edu/research/evoeco/?platform=hootsuite
http://www.necsi.edu/research/evoeco/?platform=hootsuite
🗞Measurement errors in network data
M. E. J. Newman
🔗 https://arxiv.org/pdf/1703.07376
📌 ABSTRACT
The recent growth in interest in the physics and mathematics of networks has been driven in large part by the increasing availability of data describing the structure of networks ranging from the internet and the web to social and biological networks. It is a surprising feature of many empirical studies, however, that the data are reported without any estimate of their expected accuracy, even though it is clear that most do suffer from measurement error of various kinds. In this paper we develop the theory of measurement error for network data and give an expectation-maximization algorithm for estimating both false positive and false negative rates for edges in observed networks. We give an example application of our methods to social networks determined from proximity data. The methods we describe are general, and could be extended straightforwardly to cover different types of networks or errors.
M. E. J. Newman
🔗 https://arxiv.org/pdf/1703.07376
📌 ABSTRACT
The recent growth in interest in the physics and mathematics of networks has been driven in large part by the increasing availability of data describing the structure of networks ranging from the internet and the web to social and biological networks. It is a surprising feature of many empirical studies, however, that the data are reported without any estimate of their expected accuracy, even though it is clear that most do suffer from measurement error of various kinds. In this paper we develop the theory of measurement error for network data and give an expectation-maximization algorithm for estimating both false positive and false negative rates for edges in observed networks. We give an example application of our methods to social networks determined from proximity data. The methods we describe are general, and could be extended straightforwardly to cover different types of networks or errors.