🌀 Apply now for SFI's 2017 Complex Systems Summer School
🔗 https://sficsss.fluidreview.com/
🚩The Santa Fe Institute is accepting applications for its signature education program for graduate students and postdocs: the 2017 Complex Systems Summer School, June 11-July 7, 2017, at St. John’s College in Santa Fe, New Mexico. Apply by January 23, 2017.
The program offers an intensive four-week introduction to complex behavior in mathematical, physical, living, and social systems.
CSSS is intended for graduate students and postdoctoral fellows in the sciences or social sciences who seek a background and hands-on experience conducting interdisciplinary research in complex systems.
The program includes lectures, laboratories, and discussion sessions focusing on foundational ideas, tools, and current topics in complex systems research. These include nonlinear dynamics and pattern formation, scaling theory, information theory and computation theory, adaptation and evolution, network structure and dynamics, adaptive computation techniques, and computer modeling tools and specific applications of these core topics to various disciplines.
In addition, participants will formulate and carry out team projects related to topics covered in the program.
All activities will be conducted in English.
🔗 https://sficsss.fluidreview.com/
🚩The Santa Fe Institute is accepting applications for its signature education program for graduate students and postdocs: the 2017 Complex Systems Summer School, June 11-July 7, 2017, at St. John’s College in Santa Fe, New Mexico. Apply by January 23, 2017.
The program offers an intensive four-week introduction to complex behavior in mathematical, physical, living, and social systems.
CSSS is intended for graduate students and postdoctoral fellows in the sciences or social sciences who seek a background and hands-on experience conducting interdisciplinary research in complex systems.
The program includes lectures, laboratories, and discussion sessions focusing on foundational ideas, tools, and current topics in complex systems research. These include nonlinear dynamics and pattern formation, scaling theory, information theory and computation theory, adaptation and evolution, network structure and dynamics, adaptive computation techniques, and computer modeling tools and specific applications of these core topics to various disciplines.
In addition, participants will formulate and carry out team projects related to topics covered in the program.
All activities will be conducted in English.
⭕️ Physics, Complexity and Causality
George F. R. Ellis1
🔗 http://www.nature.com.sci-hub.cc/nature/journal/v435/n7043/full/435743a.html
🐾 Although the laws of physics explain much of the world around us, we still do not have a realistic description of causality in truly complex hierarchical structures.
🐾 The atomic theory of matter and the periodic table of elements allow us to understand the physical nature of material objects, including living beings. Quantum theory illuminates the physical basis of the periodic table and the nature of chemical bonding.
George F. R. Ellis1
🔗 http://www.nature.com.sci-hub.cc/nature/journal/v435/n7043/full/435743a.html
🐾 Although the laws of physics explain much of the world around us, we still do not have a realistic description of causality in truly complex hierarchical structures.
🐾 The atomic theory of matter and the periodic table of elements allow us to understand the physical nature of material objects, including living beings. Quantum theory illuminates the physical basis of the periodic table and the nature of chemical bonding.
Complex Systems Studies
⭕️ Physics, Complexity and Causality George F. R. Ellis1 🔗 http://www.nature.com.sci-hub.cc/nature/journal/v435/n7043/full/435743a.html 🐾 Although the laws of physics explain much of the world around us, we still do not have a realistic description of…
📄 ON THE NATURE OF CAUSATION IN COMPLEX SYSTEMS
George F. R. Ellis1
🔗http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.670.2930&rep=rep1&type=pdf
George F. R. Ellis1
🔗http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.670.2930&rep=rep1&type=pdf
⭕️ The unfolding and control of network cascades
The same connections that give a network its functionality can promote the spread of failures and innovations that would otherwise remain confined.
http://physicstoday.scitation.org/doi/10.1063/PT.3.3426
The same connections that give a network its functionality can promote the spread of failures and innovations that would otherwise remain confined.
http://physicstoday.scitation.org/doi/10.1063/PT.3.3426
Complex Systems Studies
🎶 Networks Networks surround and sustain us, in nature, in our bodies, in relationships, in the digital world.
🎶 This hour, TED speakers explore how we rely on networks and how we have the power to shape them.
http://www.npr.org/programs/ted-radio-hour/
http://www.npr.org/programs/ted-radio-hour/
NPR
Who is really shaping the future of AI?
What will AI look like in 2026? Is the hype a bubble or a tech revolution that will transform everything? This episode, the global politics shaping the future of AI and what it means for you.
🌀 A history of complexity science. Update to 2020:
http://www.art-sciencefactory.com/complexity-map_feb09.html
http://www.art-sciencefactory.com/complexity-map_feb09.html
🌀 COMPLEXITY IS JUST A WORD!
BY PETER CORNING
http://complexsystems.org/publications/complexity-is-just-a-word/
BY PETER CORNING
http://complexsystems.org/publications/complexity-is-just-a-word/
🌀 THERMOECONOMICS: BEYOND THE SECOND LAW
BY PETER CORNING
🔗 http://complexsystems.org/publications/thermoeconomics-beyond-the-second-law/
📌 Abstract
Physicist Erwin Schrodinger’s What is Life? (1945) has inspired many subsequent efforts to explain biological evolution, especially the evolution of complex systems, in terms of the Second Law of Thermodynamics and the concepts of “entropy” and “negative entropy.” However, the problems associated with this paradigm are manifold. Some of these problems will be highlighted in the first part of this paper, and some of the theories that have been derived from it will be briefly critiqued. “Thermoeconomics”, by contrast, is based on the proposition that the role of energy in biological evolution should be defined and understood not in terms of the Second Law but in terms of such economic criteria as “productivity,” “efficiency,” and especially the costs and benefits (or “profitability”) of the various mechanisms for capturing and utilizing available energy to build biomass and do work. Thus thermoeconomics is fully consistent with the Darwinian paradigm. Furthermore, it is argued that economic criteria provide a better account of the advances (and recessions) in bioenergetic technologies than does any formulation derived from the Second Law.
#cybernetics, #entropy, #information, #natural_selection, #synergy, #thermodynamics
BY PETER CORNING
🔗 http://complexsystems.org/publications/thermoeconomics-beyond-the-second-law/
📌 Abstract
Physicist Erwin Schrodinger’s What is Life? (1945) has inspired many subsequent efforts to explain biological evolution, especially the evolution of complex systems, in terms of the Second Law of Thermodynamics and the concepts of “entropy” and “negative entropy.” However, the problems associated with this paradigm are manifold. Some of these problems will be highlighted in the first part of this paper, and some of the theories that have been derived from it will be briefly critiqued. “Thermoeconomics”, by contrast, is based on the proposition that the role of energy in biological evolution should be defined and understood not in terms of the Second Law but in terms of such economic criteria as “productivity,” “efficiency,” and especially the costs and benefits (or “profitability”) of the various mechanisms for capturing and utilizing available energy to build biomass and do work. Thus thermoeconomics is fully consistent with the Darwinian paradigm. Furthermore, it is argued that economic criteria provide a better account of the advances (and recessions) in bioenergetic technologies than does any formulation derived from the Second Law.
#cybernetics, #entropy, #information, #natural_selection, #synergy, #thermodynamics
Forwarded from Sitpor.org سیتپـــــور
یک سیستم پیچیده چیست؟
http://www.sitpor.org/2017/01/complexsys1/
http://www.sitpor.org/2017/01/complexsys1/
Forwarded from Sitpor.org سیتپـــــور
🌀 سیستمهای پیچیده: «ماهیت و ویژگی»
http://www.sitpor.org/2017/01/complexsys1/
🎯 مقدمه:
حدود۳۳۰ سال پیش، نیوتون با انتشار شاهکار خود، اصول ریاضی فلسفه طبیعی، نگاهی جدید نسبت به بررسی طبیعت را معرفی کرد. نگاه نیوتون به علم به کمک نظریه الکترومغناطیس که توسط مکسول جمع بندی و در نهایت توسط آلبرت اینشتین کامل شد، شالوده فیزیککلاسیک را بنا نهاد. انقلاب بعدی علم، توسط مکانیک کوانتومی رخداد. آنچه که مکانیک کوانتومی در قرن ۲۰ میلادی نشانه گرفت، مسئله موضعیت در فیزیک کلاسیک و نگاه احتمالاتی به طبیعت بود. نگاهی که سرانجام منجر به پارادایمی جدید در علم، به عنوان فیزیک مدرن شد. با این وجود، علیرغم پیشرفتهای خارقالعاده در فیزیک و سایر علوم، کماکان در توجیه بسیاری از پدیدهها وا ماندهایم. پدیدههایی که همیشه اطرافمان حاضر بودهاند ولی هیچموقع قادر به توجیه رفتار آنها نبودهایم. بنابراین، میتوان به این فکر کرد که شاید در نگاه ما به طبیعت و مسائل علمی، نقصی وجود داشته باشد. به دیگر سخن، بعید نیست که مجددا نیاز به بازنگری در نگاهمان به طبیعت (تغییر پارادایم) داشته باشیم؛ عدهی زیادی معتقدند آنچه که در قرن ۲۱ام نیاز است، نگاهی جدید به مبانی علم است؛ نگاه پیچیدگی!
http://www.sitpor.org/2017/01/complexsys1/
🎯 مقدمه:
حدود۳۳۰ سال پیش، نیوتون با انتشار شاهکار خود، اصول ریاضی فلسفه طبیعی، نگاهی جدید نسبت به بررسی طبیعت را معرفی کرد. نگاه نیوتون به علم به کمک نظریه الکترومغناطیس که توسط مکسول جمع بندی و در نهایت توسط آلبرت اینشتین کامل شد، شالوده فیزیککلاسیک را بنا نهاد. انقلاب بعدی علم، توسط مکانیک کوانتومی رخداد. آنچه که مکانیک کوانتومی در قرن ۲۰ میلادی نشانه گرفت، مسئله موضعیت در فیزیک کلاسیک و نگاه احتمالاتی به طبیعت بود. نگاهی که سرانجام منجر به پارادایمی جدید در علم، به عنوان فیزیک مدرن شد. با این وجود، علیرغم پیشرفتهای خارقالعاده در فیزیک و سایر علوم، کماکان در توجیه بسیاری از پدیدهها وا ماندهایم. پدیدههایی که همیشه اطرافمان حاضر بودهاند ولی هیچموقع قادر به توجیه رفتار آنها نبودهایم. بنابراین، میتوان به این فکر کرد که شاید در نگاه ما به طبیعت و مسائل علمی، نقصی وجود داشته باشد. به دیگر سخن، بعید نیست که مجددا نیاز به بازنگری در نگاهمان به طبیعت (تغییر پارادایم) داشته باشیم؛ عدهی زیادی معتقدند آنچه که در قرن ۲۱ام نیاز است، نگاهی جدید به مبانی علم است؛ نگاه پیچیدگی!
🗞 Power-law distributions in empirical data
Aaron Clauset, Cosma Rohilla Shalizi, M. E. J. Newman
🔗 https://arxiv.org/pdf/0706.1062v2
📌 ABSTRACT
Power-law distributions occur in many situations of scientific interest and have significant consequences for our understanding of natural and man-made phenomena. Unfortunately, the detection and characterization of power laws is complicated by the large fluctuations that occur in the tail of the distribution -- the part of the distribution representing large but rare events -- and by the difficulty of identifying the range over which power-law behavior holds. Commonly used methods for analyzing power-law data, such as least-squares fitting, can produce substantially inaccurate estimates of parameters for power-law distributions, and even in cases where such methods return accurate answers they are still unsatisfactory because they give no indication of whether the data obey a power law at all. Here we present a principled statistical framework for discerning and quantifying power-law behavior in empirical data. Our approach combines maximum-likelihood fitting methods with goodness-of-fit tests based on the Kolmogorov-Smirnov statistic and likelihood ratios. We evaluate the effectiveness of the approach with tests on synthetic data and give critical comparisons to previous approaches. We also apply the proposed methods to twenty-four real-world data sets from a range of different disciplines, each of which has been conjectured to follow a power-law distribution. In some cases we find these conjectures to be consistent with the data while in others the power law is ruled out.
Aaron Clauset, Cosma Rohilla Shalizi, M. E. J. Newman
🔗 https://arxiv.org/pdf/0706.1062v2
📌 ABSTRACT
Power-law distributions occur in many situations of scientific interest and have significant consequences for our understanding of natural and man-made phenomena. Unfortunately, the detection and characterization of power laws is complicated by the large fluctuations that occur in the tail of the distribution -- the part of the distribution representing large but rare events -- and by the difficulty of identifying the range over which power-law behavior holds. Commonly used methods for analyzing power-law data, such as least-squares fitting, can produce substantially inaccurate estimates of parameters for power-law distributions, and even in cases where such methods return accurate answers they are still unsatisfactory because they give no indication of whether the data obey a power law at all. Here we present a principled statistical framework for discerning and quantifying power-law behavior in empirical data. Our approach combines maximum-likelihood fitting methods with goodness-of-fit tests based on the Kolmogorov-Smirnov statistic and likelihood ratios. We evaluate the effectiveness of the approach with tests on synthetic data and give critical comparisons to previous approaches. We also apply the proposed methods to twenty-four real-world data sets from a range of different disciplines, each of which has been conjectured to follow a power-law distribution. In some cases we find these conjectures to be consistent with the data while in others the power law is ruled out.
☄ Postdoc in physics at Northwestern (in Adilson Motter's group) on dynamical aspects of networks (deadline 1 March)
http://dyn.phys.northwestern.edu/positions.html
http://dyn.phys.northwestern.edu/positions.html
☄ Position in Complex Networked Production Systems at Graz University of Technology
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