📄 Experimental econophysics: Complexity, selforganization, and emergent properties
J.P.Huang
Department of Physics and State Key Laboratory of Surface Physics, Fudan University, Shanghai 200433, China
🔗 http://polymer.bu.edu/hes/rp-huang15econ.pdf
📌 A B S T R A C T
Experimental econophysics is concerned with statistical physics of humans in the laboratory, and it is based on controlled human experiments developed by physicists to study some problems related toe conomics or finance. It relies on controlled human experiments in the laboratory together with agent-based modeling (for computer simulations and/or analytical theory), with an attempt to reveal the general cause-effect relationship between specific conditions and emergent properties of real economic/financial markets (a kind of complex adaptive systems). Here I #review the latest progress in the field, namely, stylized facts, herd behavior, contrarian behavior, spontaneous cooperation, partial information, and risk management. Also, I highlight the connections between such progress and other topics of traditional statistical physics. The main theme of the review is to show diverse emergent properties of the laboratory markets, originating from self-organization due to the nonlinear interactions among heterogeneous humans or agents (complexity).
J.P.Huang
Department of Physics and State Key Laboratory of Surface Physics, Fudan University, Shanghai 200433, China
🔗 http://polymer.bu.edu/hes/rp-huang15econ.pdf
📌 A B S T R A C T
Experimental econophysics is concerned with statistical physics of humans in the laboratory, and it is based on controlled human experiments developed by physicists to study some problems related toe conomics or finance. It relies on controlled human experiments in the laboratory together with agent-based modeling (for computer simulations and/or analytical theory), with an attempt to reveal the general cause-effect relationship between specific conditions and emergent properties of real economic/financial markets (a kind of complex adaptive systems). Here I #review the latest progress in the field, namely, stylized facts, herd behavior, contrarian behavior, spontaneous cooperation, partial information, and risk management. Also, I highlight the connections between such progress and other topics of traditional statistical physics. The main theme of the review is to show diverse emergent properties of the laboratory markets, originating from self-organization due to the nonlinear interactions among heterogeneous humans or agents (complexity).
🗞 Network neuroscience
Danielle S Bassett & Olaf Sporns
🔗 http://www.nature.com/neuro/journal/v20/n3/full/nn.4502.html
📌 ABSTRACT
Despite substantial recent progress, our understanding of the principles and mechanisms underlying complex brain function and cognition remains incomplete. Network neuroscience proposes to tackle these enduring challenges. Approaching brain structure and function from an explicitly integrative perspective, network neuroscience pursues new ways to map, record, analyze and model the elements and interactions of neurobiological systems. Two parallel trends drive the approach: the availability of new empirical tools to create comprehensive maps and record dynamic patterns among molecules, neurons, #brain areas and social systems; and the theoretical framework and computational tools of modern network science. The convergence of empirical and computational advances opens new frontiers of scientific inquiry, including network dynamics, manipulation and control of brain networks, and integration of network processes across spatiotemporal domains. We #review emerging trends in #network #neuroscience and attempt to chart a path toward a better understanding of the brain as a multiscale networked system.
Danielle S Bassett & Olaf Sporns
🔗 http://www.nature.com/neuro/journal/v20/n3/full/nn.4502.html
📌 ABSTRACT
Despite substantial recent progress, our understanding of the principles and mechanisms underlying complex brain function and cognition remains incomplete. Network neuroscience proposes to tackle these enduring challenges. Approaching brain structure and function from an explicitly integrative perspective, network neuroscience pursues new ways to map, record, analyze and model the elements and interactions of neurobiological systems. Two parallel trends drive the approach: the availability of new empirical tools to create comprehensive maps and record dynamic patterns among molecules, neurons, #brain areas and social systems; and the theoretical framework and computational tools of modern network science. The convergence of empirical and computational advances opens new frontiers of scientific inquiry, including network dynamics, manipulation and control of brain networks, and integration of network processes across spatiotemporal domains. We #review emerging trends in #network #neuroscience and attempt to chart a path toward a better understanding of the brain as a multiscale networked system.
#Review_article , 45 pages
🗞 Inverse statistical problems:
from the inverse Ising problem to data science
H. Chau Nguyen, Riccardo Zecchina, Johannes Berg
🔗 https://arxiv.org/pdf/1702.01522
📌 ABSTRACT
Inverse problems in statistical physics are motivated by the challenges of `big data' in different fields, in particular high-throughput experiments in biology. In inverse problems, the usual procedure of statistical physics needs to be reversed: Instead of calculating observables on the basis of model parameters, we seek to infer parameters of a model based on observations. In this review, we focus on the inverse Ising problem and closely related problems, namely how to infer the coupling strengths between spins given observed spin correlations, magnetisations, or other data. We review applications of the inverse Ising problem, including the reconstruction of neural connections, protein structure determination, and the inference of gene regulatory networks. For the inverse Ising problem in equilibrium, a number of controlled and uncontrolled approximate solutions have been developed in the statistical mechanics community. A particularly strong method, pseudolikelihood, stems from statistics. We also review the inverse Ising problem in the non-equilibrium case, where the model parameters must be reconstructed based on non-equilibrium statistics.
🗞 Inverse statistical problems:
from the inverse Ising problem to data science
H. Chau Nguyen, Riccardo Zecchina, Johannes Berg
🔗 https://arxiv.org/pdf/1702.01522
📌 ABSTRACT
Inverse problems in statistical physics are motivated by the challenges of `big data' in different fields, in particular high-throughput experiments in biology. In inverse problems, the usual procedure of statistical physics needs to be reversed: Instead of calculating observables on the basis of model parameters, we seek to infer parameters of a model based on observations. In this review, we focus on the inverse Ising problem and closely related problems, namely how to infer the coupling strengths between spins given observed spin correlations, magnetisations, or other data. We review applications of the inverse Ising problem, including the reconstruction of neural connections, protein structure determination, and the inference of gene regulatory networks. For the inverse Ising problem in equilibrium, a number of controlled and uncontrolled approximate solutions have been developed in the statistical mechanics community. A particularly strong method, pseudolikelihood, stems from statistics. We also review the inverse Ising problem in the non-equilibrium case, where the model parameters must be reconstructed based on non-equilibrium statistics.
#Review_Article on #granular_matter & #networks
🔖 Network Analysis of Particles and Grains
Lia Papadopoulos, Mason A. Porter, Karen E. Daniels, Danielle S. Bassett
🔗 https://arxiv.org/pdf/1708.08080
📌 ABSTRACT
The arrangements of particles and forces in granular materials and particulate matter have a complex organization on multiple spatial scales that range from local structures to mesoscale and system-wide ones. This multiscale organization can affect how a material responds or reconfigures when exposed to external perturbations or loading. The theoretical study of particle-level, force-chain, domain, and bulk properties requires the development and application of appropriate mathematical, statistical, physical, and computational frameworks. Traditionally, granular materials have been investigated using particulate or continuum models, each of which tends to be implicitly agnostic to multiscale organization. Recently, tools from network science have emerged as powerful approaches for probing and characterizing heterogeneous architectures in complex systems, and a diverse set of methods have yielded fascinating insights into granular materials. In this paper, we review work on network-based approaches to studying granular materials (and particulate matter more generally) and explore the potential of such frameworks to provide a useful description of these materials and to enhance understanding of the underlying physics. We also outline a few open questions and highlight particularly promising future directions in the analysis and design of granular materials and other particulate matter.
🔖 Network Analysis of Particles and Grains
Lia Papadopoulos, Mason A. Porter, Karen E. Daniels, Danielle S. Bassett
🔗 https://arxiv.org/pdf/1708.08080
📌 ABSTRACT
The arrangements of particles and forces in granular materials and particulate matter have a complex organization on multiple spatial scales that range from local structures to mesoscale and system-wide ones. This multiscale organization can affect how a material responds or reconfigures when exposed to external perturbations or loading. The theoretical study of particle-level, force-chain, domain, and bulk properties requires the development and application of appropriate mathematical, statistical, physical, and computational frameworks. Traditionally, granular materials have been investigated using particulate or continuum models, each of which tends to be implicitly agnostic to multiscale organization. Recently, tools from network science have emerged as powerful approaches for probing and characterizing heterogeneous architectures in complex systems, and a diverse set of methods have yielded fascinating insights into granular materials. In this paper, we review work on network-based approaches to studying granular materials (and particulate matter more generally) and explore the potential of such frameworks to provide a useful description of these materials and to enhance understanding of the underlying physics. We also outline a few open questions and highlight particularly promising future directions in the analysis and design of granular materials and other particulate matter.
#Review_Article on #granular_matter & #networks
🔖 Network Analysis of Particles and Grains
🔗 https://arxiv.org/pdf/1708.08080
🔖 Network Analysis of Particles and Grains
🔗 https://arxiv.org/pdf/1708.08080
Centralities in complex networks
Alexandre Bovet, Hernán A. Makse
https://arxiv.org/pdf/2105.01931
In network science complex systems are represented as a mathematical graphs consisting of a set of nodes representing the components and a set of edges representing their interactions. The framework of networks has led to significant advances in the understanding of the structure, formation and function of complex systems. Social and biological processes such as the dynamics of epidemics, the diffusion of information in social media, the interactions between species in ecosystems or the communication between neurons in our brains are all actively studied using dynamical models on complex networks. In all of these systems, the patterns of connections at the individual level play a fundamental role on the global dynamics and finding the most important nodes allows one to better understand and predict their behaviors. An important research effort in network science has therefore been dedicated to the development of methods allowing to find the most important nodes in networks. In this short #review, we describe network centrality measures based on the notions of network traversal they rely on. We limit ourselves to a limited number of centralities. The subject is much vaster than the non-exhaustive list presented here.
Alexandre Bovet, Hernán A. Makse
https://arxiv.org/pdf/2105.01931
In network science complex systems are represented as a mathematical graphs consisting of a set of nodes representing the components and a set of edges representing their interactions. The framework of networks has led to significant advances in the understanding of the structure, formation and function of complex systems. Social and biological processes such as the dynamics of epidemics, the diffusion of information in social media, the interactions between species in ecosystems or the communication between neurons in our brains are all actively studied using dynamical models on complex networks. In all of these systems, the patterns of connections at the individual level play a fundamental role on the global dynamics and finding the most important nodes allows one to better understand and predict their behaviors. An important research effort in network science has therefore been dedicated to the development of methods allowing to find the most important nodes in networks. In this short #review, we describe network centrality measures based on the notions of network traversal they rely on. We limit ourselves to a limited number of centralities. The subject is much vaster than the non-exhaustive list presented here.
#Review article: "Early warning signals for critical transitions in complex systems"
Sandip V. George, Sneha Kachhara, G. Ambika
https://arxiv.org/abs/2107.01210
In this review, we present the different measures of early warning signals that can indicate the occurrence of critical transitions in complex systems. We start with the mechanisms that trigger critical transitions, how they relate to warning signals and the methods used to detect early warning signals (EWS) for sudden transitions or tipping. We discuss briefly a few applications in real systems in this context, like transitions in ecology, climate and environment, medicine, epidemics, finance and engineering. Towards the end, we mention the issues in detecting EWS in specific applications and our perspective on future trends in this area, especially related to sudden transitions in the dynamics of connected systems on complex networks.
Sandip V. George, Sneha Kachhara, G. Ambika
https://arxiv.org/abs/2107.01210
In this review, we present the different measures of early warning signals that can indicate the occurrence of critical transitions in complex systems. We start with the mechanisms that trigger critical transitions, how they relate to warning signals and the methods used to detect early warning signals (EWS) for sudden transitions or tipping. We discuss briefly a few applications in real systems in this context, like transitions in ecology, climate and environment, medicine, epidemics, finance and engineering. Towards the end, we mention the issues in detecting EWS in specific applications and our perspective on future trends in this area, especially related to sudden transitions in the dynamics of connected systems on complex networks.
Modern computational studies of the glass transition
The physics of the glass transition and amorphous materials continues to attract the attention of a wide research community after decades of effort. Supercooled liquids and glasses have been studied numerically since the advent of molecular dynamics and Monte Carlo simulations, and computer studies have greatly enhanced both experimental discoveries and theoretical developments. In this #Review, we provide a modern perspective on this area. We describe the need to go beyond canonical methods when studying the glass transition — a problem that is notoriously difficult in terms of timescales, length scales and physical observables. We summarize recent algorithmic developments to achieve enhanced sampling and faster equilibration by using replica-exchange methods, cluster and swap Monte Carlo algorithms, and other techniques. We then review some major advances afforded by these tools regarding the statistical mechanical description of the liquid-to-glass transition, and the mechanical, vibrational and thermal properties of the glassy solid.
https://www.nature.com/articles/s42254-022-00548-x
The physics of the glass transition and amorphous materials continues to attract the attention of a wide research community after decades of effort. Supercooled liquids and glasses have been studied numerically since the advent of molecular dynamics and Monte Carlo simulations, and computer studies have greatly enhanced both experimental discoveries and theoretical developments. In this #Review, we provide a modern perspective on this area. We describe the need to go beyond canonical methods when studying the glass transition — a problem that is notoriously difficult in terms of timescales, length scales and physical observables. We summarize recent algorithmic developments to achieve enhanced sampling and faster equilibration by using replica-exchange methods, cluster and swap Monte Carlo algorithms, and other techniques. We then review some major advances afforded by these tools regarding the statistical mechanical description of the liquid-to-glass transition, and the mechanical, vibrational and thermal properties of the glassy solid.
https://www.nature.com/articles/s42254-022-00548-x
Nature
Modern computational studies of the glass transition
Nature Reviews Physics - Computer simulations may unlock crucial aspects of how a liquid transforms into a glass, but are hampered by rapidly growing relaxation times near the transition. This...