⭕️ Entropy, an idea often equated with disorder, can actually organize things:
https://www.quantamagazine.org/20170308-digital-alchemist-sharon-glotzer-interview-emergence/
https://www.quantamagazine.org/20170308-digital-alchemist-sharon-glotzer-interview-emergence/
Quanta Magazine
Digital Alchemist’ Sharon Glotzer Seeks Rules of Emergence | Quanta Magazine
Computational physicist Sharon Glotzer is uncovering the rules by which complex collective phenomena emerge from simple building blocks.
🗞 Topological Data Analysis of Financial Time Series: Landscapes of Crashes
Marian Gidea, Yuri Katz
🔗 https://arxiv.org/pdf/1703.04385
📌 ABSTRACT
We explore the evolution of daily returns of four major US stock market indices during the technology crash of 2000, and the financial crisis of 2007-2009. Our methodology is based on topological data analysis (TDA). We use persistence homology to detect and quantify topological patterns that appear in multidimensional time series. Using a sliding window, we extract time-dependent point cloud data sets, to which we associate a topological space. We detect transient loops that appear in this space, and we measure their persistence. This is encoded in real-valued functions referred to as a 'persistence landscapes'. We quantify the temporal changes in persistence landscapes via their Lp-norms. We test this procedure on multidimensional time series generated by various non-linear and non-equilibrium models. We find that, in the vicinity of financial meltdowns, the Lp-norms exhibit strong growth prior to the primary peak, which ascends during a crash. Remarkably, the average spectral density at low frequencies of the time series of Lp-norms of the persistence landscapes demonstrates a strong rising trend for 250 trading days prior to either dotcom crash on 03/10/2000, or to the Lehman bankruptcy on 09/15/2008. Our study suggests that TDA provides a new type of econometric analysis, which goes beyond the standard statistical measures. The method can be used to detect early warning signals of imminent market crashes. We believe that this approach can be used beyond the analysis of financial time series presented here.
Marian Gidea, Yuri Katz
🔗 https://arxiv.org/pdf/1703.04385
📌 ABSTRACT
We explore the evolution of daily returns of four major US stock market indices during the technology crash of 2000, and the financial crisis of 2007-2009. Our methodology is based on topological data analysis (TDA). We use persistence homology to detect and quantify topological patterns that appear in multidimensional time series. Using a sliding window, we extract time-dependent point cloud data sets, to which we associate a topological space. We detect transient loops that appear in this space, and we measure their persistence. This is encoded in real-valued functions referred to as a 'persistence landscapes'. We quantify the temporal changes in persistence landscapes via their Lp-norms. We test this procedure on multidimensional time series generated by various non-linear and non-equilibrium models. We find that, in the vicinity of financial meltdowns, the Lp-norms exhibit strong growth prior to the primary peak, which ascends during a crash. Remarkably, the average spectral density at low frequencies of the time series of Lp-norms of the persistence landscapes demonstrates a strong rising trend for 250 trading days prior to either dotcom crash on 03/10/2000, or to the Lehman bankruptcy on 09/15/2008. Our study suggests that TDA provides a new type of econometric analysis, which goes beyond the standard statistical measures. The method can be used to detect early warning signals of imminent market crashes. We believe that this approach can be used beyond the analysis of financial time series presented here.
🗞 Maximum entropy sampling in complex networks
Filippo Radicchi, Claudio Castellano
🔗 https://arxiv.org/pdf/1703.03858
📌 ABSTRACT
Many real-world systems are characterized by stochastic dynamical rules where a complex network of dependencies among individual elements probabilistically determines their state. Even with full knowledge of the network structure and of the stochastic rules of the dynamical process, the ability to predict system configurations is generally characterized by large uncertainty. Sampling a fraction of the nodes and deterministically observing their state may help to reduce the uncertainty about the unobserved nodes. However, choosing these points of observation with the goal of maximizing predictive power is a highly nontrivial task, depending on the nature of the stochastic process and on the structure of the underlying network. Here, we introduce a computationally efficient algorithm to determine quasi-optimal solutions for arbitrary stochastic processes defined on generic sparse topologies. We show that the method is effective for various processes on different substrates. We further show how the method can be fruitfully used to identify the best nodes to label in semi-supervised probabilistic classification algorithms.
Filippo Radicchi, Claudio Castellano
🔗 https://arxiv.org/pdf/1703.03858
📌 ABSTRACT
Many real-world systems are characterized by stochastic dynamical rules where a complex network of dependencies among individual elements probabilistically determines their state. Even with full knowledge of the network structure and of the stochastic rules of the dynamical process, the ability to predict system configurations is generally characterized by large uncertainty. Sampling a fraction of the nodes and deterministically observing their state may help to reduce the uncertainty about the unobserved nodes. However, choosing these points of observation with the goal of maximizing predictive power is a highly nontrivial task, depending on the nature of the stochastic process and on the structure of the underlying network. Here, we introduce a computationally efficient algorithm to determine quasi-optimal solutions for arbitrary stochastic processes defined on generic sparse topologies. We show that the method is effective for various processes on different substrates. We further show how the method can be fruitfully used to identify the best nodes to label in semi-supervised probabilistic classification algorithms.
🔹Beyond Big Data: Identifying Important Information for Real World Challenges
http://necsi.edu/projects/yaneer/information/?platform=hootsuite
http://necsi.edu/projects/yaneer/information/?platform=hootsuite
Complex Systems Studies
Offre-de-these.pdf
Interdisciplinary PhD in Cognitive and Network science at Aix-Marseille University
⭕️ PhD position open, "Temporal networks: from network theory to brain science"
http://doc2amu.univ-amu.fr/en/temporal-networks-from-network-theory-to-brain-science
http://doc2amu.univ-amu.fr/en/temporal-networks-from-network-theory-to-brain-science
http://www.biophysics.org/2017mexico/Home/tabid/6979/Default.aspx
Emerging Concepts in Ion Channel Biophysics
October 10 - 13, 2017
Mexico City, Mexico
Emerging Concepts in Ion Channel Biophysics
October 10 - 13, 2017
Mexico City, Mexico
🔹 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.