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Complex Systems Summer School 2020 | Santa Fe Institute

Fri, 11/29/2019 - 23:12

The SFI Complex Systems Summer School (CSSS) offers an intensive 4-week introduction to complex behavior in mathematical, physical, living, and social systems. Lectures are taught by the faculty of the Santa Fe Institute (SFI) and other leading educators and scholars. The school is for graduate students, postdoctoral fellows, and professionals seeking to transcend traditional disciplinary boundaries, take intellectual risks, and ask big questions about complex systems.

The program consists of an intensive series of lectures, labs, and discussions focusing on foundational concepts, tools, and current topics in complexity science. These include nonlinear dynamics, scaling theory, information theory, adaptation and evolution, networks, machine learning, agent-based models, and other topical areas and case studies. Participants collaborate in developing novel research projects throughout the four weeks of the program that culminate in final presentations and papers. 

 

June 14 – July 10, 2020

Source: santafe.edu

Complexity Measures: Open Questions and Novel Opportunities in the Automatic Design and Analysis of Robot Swarms

Thu, 11/28/2019 - 14:49

Complexity measures and information theory metrics in general have recently been attracting the interest of multi-agent and robotics communities, owing to their capability of capturing relevant features of robot behaviors, while abstracting from implementation details. We believe that theories and tools from complex systems science and information theory may be fruitfully applied in the near future to support the automatic design of robot swarms and the analysis of their dynamics. In this paper we discuss opportunities and open questions in this scenario.

 

Complexity Measures: Open Questions and Novel Opportunities in the Automatic Design and Analysis of Robot Swarms
Andrea Roli1, Antoine Ligot and Mauro Birattari

Front. Robot. AI, 26 November 2019

 

Source: www.frontiersin.org

Hidden complexity in Life-like rules

Thu, 11/28/2019 - 10:00

An alternative way to study the rules of life-like cellular automata is presented. The proposed perspective studies some multifractal and informational properties of Boolean functions behind these rules. Results from this approach challenge the traditional argument about the simplicity of Lifelike rules.

 

Hidden complexity in Life-like rules

Miguel Melgarejo, Marco Alzate, and Nelson Obregon
Phys. Rev. E 100, 052133

Source: journals.aps.org

The social physics collective

Sun, 11/24/2019 - 17:34

More than two centuries ago Henri de Saint-Simon envisaged physical laws to describe human societies. Driven by advances in statistical physics, network science, data analysis, and information technology, this vision is becoming a reality. Many of the grandest challenges of our time are of a societal nature, and methods of physics are increasingly playing a central role in improving our understanding of these challenges, and helping us to find innovative solutions. The Social physics Collection at Scientific Reports is dedicated to this research.

 

The social physics collective

Matjaž Perc
Scientific Reports volume 9, Article number: 16549 (2019)

Source: www.nature.com

Temporal Network Theory

Sun, 11/24/2019 - 14:01

This book focuses on the theoretical side of temporal network research and gives an overview of the state of the art in the field. Curated by two pioneers in the field who have helped to shape it, the book contains contributions from many leading researchers. Temporal networks fill the border area between network science and time-series analysis and are relevant for the modeling of epidemics, optimization of transportation and logistics, as well as understanding biological phenomena.

Network theory has proven, over the past 20 years to be one of the most powerful tools for the study and analysis of complex systems. Temporal network theory is perhaps the most recent significant development in the field in recent years, with direct applications to many of the "big data" sets. This monograph will appeal to students, researchers and professionals alike interested in theory and temporal networks, a field that has grown tremendously over the last decade.

 

Temporal Network Theory
Editors: Holme, Petter, Saramäki, Jari 

Source: www.springer.com

We Shouldn’t be Scared by ‘Superintelligent A.I.’

Fri, 11/22/2019 - 14:32

Intelligent machines catastrophically misinterpreting human desires is a frequent trope in science fiction, perhaps used most memorably in Isaac Asimov’s stories of robots that misconstrue the famous “three laws of robotics.” The idea of artificial intelligence going awry resonates with human fears about technology. But current discussions of superhuman A.I. are plagued by flawed intuitions about the nature of intelligence.

Source: www.nytimes.com

Reward and punishment in climate change dilemmas

Fri, 11/22/2019 - 13:56

Mitigating climate change effects involves strategic decisions by individuals that may choose to limit their emissions at a cost. Everyone shares the ensuing benefits and thereby individuals can free ride on the effort of others, which may lead to the tragedy of the commons. For this reason, climate action can be conveniently formulated in terms of Public Goods Dilemmas often assuming that a minimum collective effort is required to ensure any benefit, and that decision-making may be contingent on the risk associated with future losses. Here we investigate the impact of reward and punishment in this type of collective endeavors — coined as collective-risk dilemmas — by means of a dynamic, evolutionary approach. We show that rewards (positive incentives) are essential to initiate cooperation, mostly when the perception of risk is low. On the other hand, we find that sanctions (negative incentives) are instrumental to maintain cooperation. Altogether, our results are gratifying, given the a-priori limitations of effectively implementing sanctions in international agreements. Finally, we show that whenever collective action is most challenging to succeed, the best results are obtained when both rewards and sanctions are synergistically combined into a single policy.

 

Reward and punishment in climate change dilemmas
António R. Góis, Fernando P. Santos, Jorge M. Pacheco & Francisco C. Santos 
Scientific Reports volume 9, Article number: 16193 (2019)

Source: www.nature.com

César Hidalgo on Information in Societies, Economies, and the Universe

Fri, 11/22/2019 - 11:38

Maxwell’s Demon is a famous thought experiment in which a mischievous imp uses knowledge of the velocities of gas molecules in a box to decrease the entropy of the gas, which could then be used to do useful work such as pushing a piston. This is a classic example of converting information (what the gas molecules are doing) into work. But of course that kind of phenomenon is much more widespread — it happens any time a company or organization hires someone in order to take advantage of their know-how. César Hidalgo has become an expert in this relationship between information and work, both at the level of physics and how it bubbles up into economies and societies. Looking at the world through the lens of information brings new insights into how we learn things, how economies are structured, and how novel uses of data will transform how we live.

Source: www.preposterousuniverse.com

On Markov blankets and hierarchical self-organisation

Thu, 11/21/2019 - 16:26

Biological self-organisation can be regarded as a process of spontaneous pattern formation; namely, the emergence of structures that distinguish themselves from their environment. This process can occur at nested spatial scales: from the microscopic (e.g., the emergence of cells) to the macroscopic (e.g. the emergence of organisms). In this paper, we pursue the idea that Markov blankets – that separate the internal states of a structure from external states – can self-assemble at successively higher levels of organisation. Using simulations, based on the principle of variational free energy minimisation, we show that hierarchical self-organisation emerges when the microscopic elements of an ensemble have prior (e.g., genetic) beliefs that they participate in a macroscopic Markov blanket: i.e., they can only influence – or be influenced by – a subset of other elements. Furthermore, the emergent structures look very much like those found in nature (e.g., cells or organelles), when influences are mediated by short range signalling. These simulations are offered as a proof of concept that hierarchical self-organisation of Markov blankets (into Markov blankets) can explain the self-evidencing, autopoietic behaviour of biological systems.

 

On Markov blankets and hierarchical self-organisation
Ensor Rafael Palacios, Adeel Razi, Thomas Parr, Michael Kirchhoff, Karl Friston

Journal of Theoretical Biology

Source: www.sciencedirect.com

Generalizing RNA velocity to transient cell states through dynamical modeling

Thu, 11/21/2019 - 14:30

The introduction of RNA velocity in single cells has opened up new ways of studying cellular differentiation. The originally proposed framework obtains velocities as the deviation of the observed ratio of spliced and unspliced mRNA from an inferred steady state. Errors in velocity estimates arise if the central assumptions of a common splicing rate and the observation of the full splicing dynamics with steady-state mRNA levels are violated. With scVelo (https://scvelo.org), we address these restrictions by solving the full transcriptional dynamics of splicing kinetics using a likelihood-based dynamical model. This generalizes RNA velocity to a wide variety of systems comprising transient cell states, which are common in development and in response to perturbations. We infer gene-specific rates of transcription, splicing and degradation, and recover the latent time of the underlying cellular processes. This latent time represents the cell’s internal clock and is based only on its transcriptional dynamics. Moreover, scVelo allows us to identify regimes of regulatory changes such as stages of cell fate commitment and, therein, systematically detects putative driver genes. We demonstrate that scVelo enables disentangling heterogeneous subpopulation kinetics with unprecedented resolution in hippocampal dentate gyrus neurogenesis and pancreatic endocrinogenesis. We anticipate that scVelo will greatly facilitate the study of lineage decisions, gene regulation, and pathway activity identification.

 

Generalizing RNA velocity to transient cell states through dynamical modeling
Volker Bergen, Marius Lange, Stefan Peidli, F. Alexander Wolf, Fabian J. Theis

Source: www.biorxiv.org

Nonlinearity + Networks: A 2020 Vision

Thu, 11/21/2019 - 13:58

I briefly survey several fascinating topics in networks and nonlinearity. I highlight a few methods and ideas, including several of personal interest, that I anticipate to be especially important during the next several years. These topics include temporal networks (in which the entities and/or their interactions change in time), stochastic and deterministic dynamical processes on networks, adaptive networks (in which a dynamical process on a network is coupled to dynamics of network structure), and network structure and dynamics that include "higher-order" interactions (which involve three or more entities in a network). I draw examples from a variety of scenarios, including contagion dynamics, opinion models, waves, and coupled oscillators.

 

Nonlinearity + Networks: A 2020 Vision
Mason A. Porter

Source: arxiv.org

Is the World Chaos, a Machine, or Evolving Complexity? How Well Can We Understand Life and World Affairs?

Thu, 11/21/2019 - 10:23

Chaos, machine, or evolving complexity? The butterfly effect suggests a world in chaos—with linkages so random or nuanced that just to measure or pre-state them is virtually impossible. To predict how they will interact is even less feasible. Thanks to “adjacent possibles” and the contradictory impulses of human behavior, much of our world appears to move in random spasms. Every new technology and policy outcome creates opportunities to push society in new and often unforeseen directions, driven by human agents who may introduce crucial but unpredictable goals, strategies, and actions. Against this view, complexity science seeks to identify patterns in interactive relationships. Many patterns can be plotted and, in some cases, foreseen. A comparison of political entities across the globe points to certain factors conducing to societal fitness. Analysis of states that have declined in fitness suggests why their strengths turned to weaknesses. A survey of societies that were relatively democratic points to several factors that contributed to their acquiring authoritarian regimes. Scientists and scholars can unveil some elements of order but should strive to do so without hubris. Wise policymakers will strive to channel both the “actuals” and “adjacent possibles” that then arise toward constructive futures.

 

NETSOL: New Trends in Social and Liberal Sciences

Year: 2019 / Volume : 2 / Area: Interdisciplinary Studies

Walter C. Clemens and Stuart A. Kauffman
Is the World Chaos, a Machine, or Evolving Complexity? How Well Can We Understand Life and World Affairs? pp.24-43.

Source: www.netsoljournal.net

Data-driven discovery of coordinates and governing equations

Wed, 11/20/2019 - 13:55

Governing equations are essential to the study of physical systems, providing models that can generalize to predict previously unseen behaviors. There are many systems of interest across disciplines where large quantities of data have been collected, but the underlying governing equations remain unknown. This work introduces an approach to discover governing models from data. The proposed method addresses a key limitation of prior approaches by simultaneously discovering coordinates that admit a parsimonious dynamical model. Developing parsimonious and interpretable governing models has the potential to transform our understanding of complex systems, including in neuroscience, biology, and climate science.

 

Data-driven discovery of coordinates and governing equations
Kathleen Champion, Bethany Lusch, J. Nathan Kutz, and Steven L. Brunton
PNAS November 5, 2019 116 (45) 22445-22451; first published October 21, 2019 https://doi.org/10.1073/pnas.1906995116

Source: www.pnas.org

Quantifying the dynamics of failure across science, startups and security

Sun, 11/17/2019 - 12:54

Human achievements are often preceded by repeated attempts that fail, but little is known about the mechanisms that govern the dynamics of failure. Here, building on previous research relating to innovation1,2,3,4,5,6,7, human dynamics8,9,10,11 and learning12,13,14,15,16,17, we develop a simple one-parameter model that mimics how successful future attempts build on past efforts. Solving this model analytically suggests that a phase transition separates the dynamics of failure into regions of progression or stagnation and predicts that, near the critical threshold, agents who share similar characteristics and learning strategies may experience fundamentally different outcomes following failures. Above the critical point, agents exploit incremental refinements to systematically advance towards success, whereas below it, they explore disjoint opportunities without a pattern of improvement. The model makes several empirically testable predictions, demonstrating that those who eventually succeed and those who do not may initially appear similar, but can be characterized by fundamentally distinct failure dynamics in terms of the efficiency and quality associated with each subsequent attempt. We collected large-scale data from three disparate domains and traced repeated attempts by investigators to obtain National Institutes of Health (NIH) grants to fund their research, innovators to successfully exit their startup ventures, and terrorist organizations to claim casualties in violent attacks. We find broadly consistent empirical support across all three domains, which systematically verifies each prediction of our model. Together, our findings unveil detectable yet previously unknown early signals that enable us to identify failure dynamics that will lead to ultimate success or failure. Given the ubiquitous nature of failure and the paucity of quantitative approaches to understand it, these results represent an initial step towards the deeper understanding of the complex dynamics underlying failure.

 

Quantifying the dynamics of failure across science, startups and security
Yian Yin, Yang Wang, James A. Evans & Dashun Wang 
Nature volume 575, pages190–194(2019)

Source: www.nature.com

Nature’s reach: narrow work has broad impact

Sat, 11/16/2019 - 13:00

How knowledge informs and alters disciplines is itself an enlightening, and vibrant field1. This type of meta research into new findings, insights, conceptual frameworks and techniques is important, among other things, for policymakers who fund research in the hope of tackling society’s most pressing challenges, which inevitably span disciplines.

Since its founding in 1869, Nature has offered a venue for publishing major advances from many fields. To mark its anniversary, we track here how papers cite and are cited across disciplines, using data on tens of millions of scientific articles indexed in Clarivate Analytics’ Web of Science (WoS), a bibliometric database that encompasses many thousands of research journals starting from 1900. We pay particular attention to articles that appeared in Nature. In our view, this snapshot, for all its idiosyncrasies, reveals how scientific work is ever more becoming a mixture of disciplines.

 

Nature’s reach: narrow work has broad impact
A scientific paper today is inspired by more disciplines than ever before, shows a new analysis marking the journal’s 150th anniversary.
Alexander J. Gates, Qing Ke, Onur Varol & Albert-László Barabási

Source: www.nature.com

Ethics and Complexity: Why standard ethical frameworks cannot cope with socio-technological change

Thu, 11/14/2019 - 13:12

Standard ethical frameworks struggle to deal with transhumanism, ecological issues and the rising technodiversity because they are focused on guiding and evaluating human behavior. Ethics needs its Copernican revolution to be able to deal with all moral agents, including not only humans, but also artificial intelligent agents, robots or organizations of all sizes. We argue that embracing the complexity worldview is the first step towards this revolution, and that standard ethical frameworks are still entrenched in the Newtonian worldview. We first spell out the foundational assumptions of the Newtonian worldview, where all change is reduced to material particles following predetermined trajectories governed by the laws of nature. However, modern physical theories such as relativity, quantum mechanics, chaos theory and thermodynamics have drawn a much more confusing and uncertain picture, and inspired indecisive, subjectivist, relativist, nihilist or postmodern worldviews. Based on cybernetics, systems theory and the new sciences of complexity, we introduce the complexity worldview that sees the world as interactions and their emergent organizations. We use this complexity worldview to show the limitations of standard ethical frameworks such as deontology, theology, consequentialism, virtue ethics, evolutionary ethics and pragmatism. Keywords: Complexity, philosophy, ethics, cybernetics, transhumanism, universal ethics, systems ethics.

 

 

Ethics and Complexity: Why standard ethical frameworks cannot cope with socio-technological change
Clément Vidal & Francis Heylighen

Source: philpapers.org

Predicting Urban Innovation from the Workforce Mobility Network in US

Wed, 11/13/2019 - 12:58

While great emphasis has been placed on the role of social interactions as driver of innovation growth, very few empirical studies have explicitly investigated the impact of social network structures on the innovation performance of cities. Past research has mostly explored scaling laws of socio-economic outputs of cities as determined by, for example, the single predictor of population. Here, by drawing on a publicly available dataset of the startup ecosystem, we build the first Workforce Mobility Network among US metropolitan areas. We found that node centrality computed on this network accounts for most of the variability observed in cities’ innovation performance and significantly outperforms other predictors such as population size or density, suggesting that policies and initiatives aiming at sustaining innovation processes might benefit from fostering professional networks alongside other economic or systemic incentives. As opposed to previous approaches powered by census data, our model can be updated in real-time upon open databases, opening up new opportunities both for researchers in a variety of disciplines to study urban economies in new ways, and for practitioners to design tools for monitoring such economies in real-time.

 

Predicting Urban Innovation from the Workforce Mobility Network in US
Moreno Bonaventura, Luca Maria Aiello, Daniele Quercia, Vito Latora

Source: arxiv.org

Information Spreading on Weighted Multiplex Social Network

Tue, 11/12/2019 - 13:22

Information spreading on multiplex networks has been investigated widely. For multiplex networks, the relations of each layer possess different extents of intimacy, which can be described as weighted multiplex networks. Nevertheless, the effect of weighted multiplex network structures on information spreading has not been analyzed comprehensively. We herein propose an information spreading model on a weighted multiplex network. Then, we develop an edge-weight-based compartmental theory to describe the spreading dynamics. We discover that under any adoption threshold of two subnetworks, reducing weight distribution heterogeneity does not alter the growth pattern of the final adoption size versus information transmission probability while accelerating information spreading. For fixed weight distribution, the growth pattern changes with the heterogeneous of degree distribution. There is a critical initial seed size, below which no global information outbreak can occur. Extensive numerical simulations affirm that the theoretical predictions agree well with the numerical results.

 

Information Spreading on Weighted Multiplex Social Network
Xuzhen Zhu, Jinming Ma, Xin Su, Hui Tian, Wei Wang, and Shimin Cai

Complexity
Volume 2019, Article ID 5920187, 15 pages
https://doi.org/10.1155/2019/5920187

Source: www.hindawi.com

Non-thermal fixed points: Universal dynamics far from equilibrium

Tue, 11/12/2019 - 12:56

In this article we give an overview of the concept of universal dynamics near non-thermal fixed points in isolated quantum many-body systems. We outline a non-perturbative kinetic theory derived within a Schwinger-Keldysh closed-time path-integral approach, as well as a low-energy effective field theory which enable us to predict the universal scaling exponents characterizing the time evolution at the fixed point. We discuss the role of wave-turbulent transport in the context of such fixed points and discuss universal scaling evolution of systems bearing ensembles of (quasi) topological defects. This is rounded off by the recently introduced concept of prescaling as a generic feature of the evolution towards a non-thermal fixed point.

 

Non-thermal fixed points: Universal dynamics far from equilibrium
Christian-Marcel Schmied, Aleksandr N. Mikheev, Thomas Gasenzer

Source: arxiv.org

Challenges for the Periodic Systems of Elements: Chemical, Historical and Mathematical Perspectives

Mon, 11/11/2019 - 17:20

We celebrate 150 years of periodic systems that reached their maturity in the 1860s. They began as pedagogical efforts to project corpuses of substances on the similarity and order relationships of the chemical elements. However, these elements are not the canned substances wrongly displayed in many periodic tables, but rather the abstract preserved entities in compound transformations. We celebrate the systems, rather than their tables or ultimate table. The periodic law, we argue, is not an all‐encompassing achievement, as it does not apply to every property of all elements and compounds. Periodic systems have been generalised as ordered hypergraphs, which solves the long‐lasting question on the mathematical structure of the systems. In this essay, it is shown that these hypergraphs may solve current issues such as order reversals in super‐heavy elements and lack of system predictive power. We discuss research in extending the limits of the systems in the super‐heavy‐atom region and draw attention to other limits: the antimatter region and the limit arising from compounds under extreme conditions. As systems depend on the known chemical substances (chemical space) and such a space grows exponentially, we wonder whether systems still aim at projecting knowledge of compounds on the relationships among the elements. We claim that systems are not based on compounds anymore, rather on 20th century projections of the 1860s systems of elements on systems of atoms. These projections bring about oversimplifications based on entities far from being related to compounds. A linked oversimplification is the myth of vertical group similarity, which raises questions on the approaches to locate new elements in the system. Finally, we propose bringing back chemistry to the systems by exploring similarity and order relationships of elements using the current information of the chemical space. We ponder whether 19th century periodic systems are still there or whether they have faded away, leaving us with an empty 150th celebration.

 

Challenges for the Periodic Systems of Elements: Chemical, Historical and Mathematical Perspective

Guillermo Restrepo

Chemistry – A European Journal

Source: onlinelibrary.wiley.com

Pages