📖 Phenomenological theory of collective decision-making
Anna Zafeiris, Zsombor Koman, Enys Mones, Tamás Vicsek
https://arxiv.org/pdf/1612.00071v1
🔗 ABSTRACT
An essential task of groups is to provide efficient solutions for the complex problems they face. Indeed, considerable efforts have been devoted to the question of collective decision-making related to problems involving a single dominant feature. Here we introduce a quantitative formalism for finding the optimal distribution of the group members' competences in the more typical case when the underlying problem is complex, i.e., multidimensional. Thus, we consider teams that are aiming at obtaining the best possible answer to a problem having a number of independent sub-problems. Our approach is based on a generic scheme for the process of evaluating the proposed solutions (i.e., negotiation). We demonstrate that the best performing groups have at least one specialist for each sub-problem -- but a far less intuitive result is that finding the optimal solution by the interacting group members requires that the specialists also have some insight into the sub-problems beyond their unique field(s). We present empirical results obtained by using a large-scale database of citations being in good agreement with the above theory. The framework we have developed can easily be adapted to a variety of realistic situations since taking into account the weights of the sub-problems, the opinions or the relations of the group is straightforward. Consequently, our method can be used in several contexts, especially when the optimal composition of a group of decision-makers is designed.
Subjects: #Physics and #Society (physics.soc-ph); Social and #Information #Networks
Anna Zafeiris, Zsombor Koman, Enys Mones, Tamás Vicsek
https://arxiv.org/pdf/1612.00071v1
🔗 ABSTRACT
An essential task of groups is to provide efficient solutions for the complex problems they face. Indeed, considerable efforts have been devoted to the question of collective decision-making related to problems involving a single dominant feature. Here we introduce a quantitative formalism for finding the optimal distribution of the group members' competences in the more typical case when the underlying problem is complex, i.e., multidimensional. Thus, we consider teams that are aiming at obtaining the best possible answer to a problem having a number of independent sub-problems. Our approach is based on a generic scheme for the process of evaluating the proposed solutions (i.e., negotiation). We demonstrate that the best performing groups have at least one specialist for each sub-problem -- but a far less intuitive result is that finding the optimal solution by the interacting group members requires that the specialists also have some insight into the sub-problems beyond their unique field(s). We present empirical results obtained by using a large-scale database of citations being in good agreement with the above theory. The framework we have developed can easily be adapted to a variety of realistic situations since taking into account the weights of the sub-problems, the opinions or the relations of the group is straightforward. Consequently, our method can be used in several contexts, especially when the optimal composition of a group of decision-makers is designed.
Subjects: #Physics and #Society (physics.soc-ph); Social and #Information #Networks
🎯 Samuel Arbesman on #Complex_Adaptive_Systems and the Difference between #Biological and #Physics Based Thinking
https://www.farnamstreetblog.com/2016/11/samuel-arbesman-biological-physics-thinking/?utm_source=twitter.com&utm_medium=social&utm_campaign=buffer&utm_content=bufferb2052
https://www.farnamstreetblog.com/2016/11/samuel-arbesman-biological-physics-thinking/?utm_source=twitter.com&utm_medium=social&utm_campaign=buffer&utm_content=bufferb2052
Farnam Street
Samuel Arbesman on Complex Adaptive Systems and the Difference between Biological and Physics Based Thinking
Knowledge Project and Shane Parrish. Samuel Arbesman (@arbesman) is a complexity scientist focusing on the nature of scientific and technological change.
📄 Universality of the SIS prevalence in networks
Piet Van Mieghem
https://arxiv.org/pdf/1612.01386v1
📌 ABSTRACT
Epidemic models are increasingly used in real-world networks to understand diffusion phenomena (such as the spread of diseases, emotions, innovations, failures) or the transport of information (such as news, memes in social on-line networks). A new analysis of the prevalence, the expected number of infected nodes in a network, is presented and physically interpreted. The analysis method is based on spectral decomposition and leads to a universal, analytic curve, that can bound the time-varying prevalence in any finite time interval. Moreover, that universal curve also applies to various types of Susceptible-Infected-Susceptible (SIS) (and Susceptible-Infected-Removed (SIR)) infection processes, with both homogenous and heterogeneous infection characteristics (curing and infection rates), in temporal and even disconnected graphs and in SIS processes with and without self-infections. The accuracy of the universal curve is comparable to that of well-established mean-field approximations.
Subjects: #Physics and #Society (physics.soc-ph); #Social and #Information #Networks (cs.SI); #Populations and #Evolution (q-bio.PE)
Piet Van Mieghem
https://arxiv.org/pdf/1612.01386v1
📌 ABSTRACT
Epidemic models are increasingly used in real-world networks to understand diffusion phenomena (such as the spread of diseases, emotions, innovations, failures) or the transport of information (such as news, memes in social on-line networks). A new analysis of the prevalence, the expected number of infected nodes in a network, is presented and physically interpreted. The analysis method is based on spectral decomposition and leads to a universal, analytic curve, that can bound the time-varying prevalence in any finite time interval. Moreover, that universal curve also applies to various types of Susceptible-Infected-Susceptible (SIS) (and Susceptible-Infected-Removed (SIR)) infection processes, with both homogenous and heterogeneous infection characteristics (curing and infection rates), in temporal and even disconnected graphs and in SIS processes with and without self-infections. The accuracy of the universal curve is comparable to that of well-established mean-field approximations.
Subjects: #Physics and #Society (physics.soc-ph); #Social and #Information #Networks (cs.SI); #Populations and #Evolution (q-bio.PE)
🗞 #Physics, #Emergence, and the #Connectome
Robert B. Laugh
Department of Physics, Stanford University, Stanford, CA
🔗 http://www.cell.com/neuron/pdf/S0896-6273(14)00681-3.pdf
📌 SUMMARY:
Experience with complex systems more primitive than the brain teaches important lessons about big data in biology. Chief among them is that physical laws, relationships among measured things that are always true, emerge out of chaos, not the other way around. Correct prediction (as opposed to incorrect prediction) from large data sets requires understanding of these laws. The reason is that the same processes that make them also make the system wildly error-intolerant if the errors are too large. This instability routinely causes computer simulations of even primitive systems to fail by enabling mistakes to cascade into ever worsening falsehoods. The more complex and sophisticated the system is, the more intolerant to errors it becomes.
Robert B. Laugh
Department of Physics, Stanford University, Stanford, CA
🔗 http://www.cell.com/neuron/pdf/S0896-6273(14)00681-3.pdf
📌 SUMMARY:
Experience with complex systems more primitive than the brain teaches important lessons about big data in biology. Chief among them is that physical laws, relationships among measured things that are always true, emerge out of chaos, not the other way around. Correct prediction (as opposed to incorrect prediction) from large data sets requires understanding of these laws. The reason is that the same processes that make them also make the system wildly error-intolerant if the errors are too large. This instability routinely causes computer simulations of even primitive systems to fail by enabling mistakes to cascade into ever worsening falsehoods. The more complex and sophisticated the system is, the more intolerant to errors it becomes.
🎞 http://www.cornell.edu/video/ben-widom-potential-distribution-theory-statistical-mechanics
Ben Widom, Cornell's Goldwin Smith Professor of Chemistry, discusses potential-distribution theory in statistical mechanics, Feb. 20, 2017.
#physics #chemistry
Ben Widom, Cornell's Goldwin Smith Professor of Chemistry, discusses potential-distribution theory in statistical mechanics, Feb. 20, 2017.
#physics #chemistry
CornellCast
Potential-Distribution Theory in Statistical Mechanics - CornellCast
Ben Widom, Cornell's Goldwin Smith Professor of Chemistry, discusses potential-distribution theory in statistical mechanics, Feb. 20, 2017.
🔻 Take a look at this #PhD #Fellowship in #LivingMatter #Physics offered at MPI for Dynamics and Self-Organization
https://t.co/QjwgRmYKcY #PhDGermany #TheoreticalPhysics #AppliedMathematics
https://t.co/QjwgRmYKcY #PhDGermany #TheoreticalPhysics #AppliedMathematics
www.daad.de
PhDGermany: PhD positions (m/f) in Living Matter Physics
Open PhD Position in: Mathematics / Natural Sciences
In the newly established Department of Living Matter Physics (LMP) we seek to fill a number of PhD positions (m/f).
The Max Planck Institute for Dynamics and Self-Organization (MPI-DS) at Göttingen,…
In the newly established Department of Living Matter Physics (LMP) we seek to fill a number of PhD positions (m/f).
The Max Planck Institute for Dynamics and Self-Organization (MPI-DS) at Göttingen,…
〽️The statistical mechanics of Twitter
🌐Paper : https://arxiv.org/abs/1812.07029
🎲 @ComplexSys
#Physics #Society #StatisticPhysics
🌐Paper : https://arxiv.org/abs/1812.07029
🎲 @ComplexSys
#Physics #Society #StatisticPhysics
A new theory derived from classical #physics allows scientists to predict how #economies worldwide respond to major disturbances such as the 2008 Great Recession or Trump tariffs:
https://t.co/JHsp7YD1bg
#economics #CSHVienna
https://t.co/JHsp7YD1bg
#economics #CSHVienna
👨🎓 #Education | Applications to our #MSc "Computational and Mathematical #Biology" and "#Physics of Complex Systems" (in Msc Physique) are open to EU students until June 10, 2019!
To apply: https://t.co/D9E8epHuLM
More info about our MSc: https://t.co/GzgDdVBD3f
#Marseille
To apply: https://t.co/D9E8epHuLM
More info about our MSc: https://t.co/GzgDdVBD3f
#Marseille
Centuri Living Systems
Education - Centuri Living Systems
Education The next generation of life scientists will be at the interface between biology, physics, mathematics and computer science. To prepare students for this transition, CENTURI is setting up interdisciplinary Masters and Doctorate level training programmes.…
Statistical physics and machine learning with David J. Schwab, (The Graduate Center, CUNY)
Part I:
Part II:
🔸About the lecture
🔹 Adventures in the Theoretical Sciences
Part I:
Part II:
🔸About the lecture
🔹 Adventures in the Theoretical Sciences
YouTube
Part 1: Statistical physics and machine learning with David J. Schwab
June 18, 2020 "Statistical physics and machine learning" David J. Schwab (The Graduate Center, CUNY). Adventures in the Theoretical Sciences is an informal, online summer lecture series for advanced undergraduate and graduate students. #AdventuresinTheory…
🎞 فیزیک آماری و یادگیری ماشین - David J. Schwab
قسمت اول
قسمت دوم
June 19, 2020 "Statistical physics and machine learning" with David J. Schwab (The Graduate Center, CUNY). Adventures in the Theoretical Sciences is an informal, online summer lecture series for advanced undergraduate and graduate students.
https://itsatcuny.org/summerschool/schwab-lecture
قسمت اول
قسمت دوم
June 19, 2020 "Statistical physics and machine learning" with David J. Schwab (The Graduate Center, CUNY). Adventures in the Theoretical Sciences is an informal, online summer lecture series for advanced undergraduate and graduate students.
https://itsatcuny.org/summerschool/schwab-lecture
آپارات - سرویس اشتراک ویدیو
فیزیک آماری و یادگیری ماشین ۱ - David J. Schwab
June 18, 2020 "Statistical physics and machine learning" David J. Schwab (The Graduate Center, CUNY). Adventures in the Theoretical Sciences is an informal, online summer lecture series for advanced undergraduate and graduate students. #AdventuresinTheory…
#Event | 12 days left to register to the free online symposium #Physics of Living Matter 15! 🕒
➡ More info: https://t.co/CXuqQrLn1S
#Biology #Physics #Biophysics #science #PLM15
➡ More info: https://t.co/CXuqQrLn1S
#Biology #Physics #Biophysics #science #PLM15
Are you a young physicist or mathematician looking for a life-changing opportunity? ICTP is now accepting #applications for the 2021-2022 class of its Postgraduate #Diploma Programme!
Click here to find out more and apply online 👉 https://t.co/wLuULx9Vog
#science #physics #math
Click here to find out more and apply online 👉 https://t.co/wLuULx9Vog
#science #physics #math
Are you a young physicist or mathematician looking for a life-changing opportunity? ICTP is now accepting #applications for the 2021-2022 class of its Postgraduate #Diploma Programme!
Click here to find out more and apply online 👉 https://t.co/wLuULx9Vog
#complex_systems #physics #math
Click here to find out more and apply online 👉 https://t.co/wLuULx9Vog
#complex_systems #physics #math
The Workshop on #Stochastic #Thermodynamics returns this May 17-21 to explore applications to #Physics, #Chemistry, #Biophysics, #InformationProcessing, #QuantumComputing, and more.
View program info, register, and submit abstracts:
https://wiki.santafe.edu/index.php/Stoc
View program info, register, and submit abstracts:
https://wiki.santafe.edu/index.php/Stoc
💰 Opening for a fully funded #PhD position in Applied #Physics & Complex Systems
at ComuneLab in collaboration with Bologna University
to work on the StatPhys of human #Mobility in urban systems
Deadline: 20 May
INFOS 👉 https://t.co/1d8XYtbBJL
at ComuneLab in collaboration with Bologna University
to work on the StatPhys of human #Mobility in urban systems
Deadline: 20 May
INFOS 👉 https://t.co/1d8XYtbBJL
2 #postdoc s 2.5y @CUDANLab in audiovisual machine learning & cultural dynamics. Apply by May 31! > https://t.co/uBqcVjJFK5
Work with socio-cultural #corpora / #BigData using #CulturalAnalytics #CS #ComputerVision #ML #Physics #CompSocSci #NetSci #NLProc #Stats #DataScience #DH https://t.co/HFsyiszkNo
Work with socio-cultural #corpora / #BigData using #CulturalAnalytics #CS #ComputerVision #ML #Physics #CompSocSci #NetSci #NLProc #Stats #DataScience #DH https://t.co/HFsyiszkNo
We have open positions (PhD and Postdoc) in theoretical biophysics! Come and join us in Erlangen/Bavaria/Germany!
#Physics #PhD #postdoc https://t.co/8pp1DmXesO
#Physics #PhD #postdoc https://t.co/8pp1DmXesO
Looking for a #postdoc in computational mechanics applied to #materials #physics and #biophysics. You'll also have the opportunity to learn and develop scientific #MachineLearning methods related to these problems. Check us out: https://t.co/df5oPmTm0C