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

Complexity Digest - 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

Complexity Digest - 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

Complexity Digest - 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

Complexity Digest - 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

Complexity Digest - 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

Complexity Digest - 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

Complexity Digest - 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

Complexity Digest - 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

Complexity Digest - 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

Collective Intelligence 2020

Complexity Digest - Mon, 11/11/2019 - 15:23

The ACM Collective Intelligence 2020 is the eighth edition of this annual interdisciplinary conference sponsored by SIGCHI dedicated to advancing our understanding of collective intelligence and the workings of teams. The conference will take place at Northeastern University in Boston, MA on June 18-19, 2020.

Coming from myriad disciplines and fields, conference participants share how connecting groups of people, information, and machines can lead to more intelligent behavior and more effective problem solving.

The annual interdisciplinary conference that brings together researchers from the academy, businesses, non-profits, governments and the world at large to share insights and ideas from a variety of fields relevant to understanding and designing collective intelligence in its many forms.

Source: ci2020.weebly.com

NERCCS 2020: Third Northeast Regional Conference on Complex Systems

Complexity Digest - Mon, 11/11/2019 - 13:05

NERCCS 2020: The Third Northeast Regional Conference on Complex Systems will follow the success of NERCCS 2019 and NERCCS 2018 to promote the emerging venue of interdisciplinary scholarly exchange for complex systems researchers in the Northeast U.S. region to share their research outcomes through presentations and post-conference online publications, network with their peers in the region, and promote inter-campus collaboration and the growth of the research community.

NERCCS will particularly focus on facilitating the professional growth of early career faculty, postdocs, and students in the region who will likely play a leading role in the field of complex systems science and engineering in the coming years.

 

NERCCS 2020: Third Northeast Regional Conference on Complex Systems at University at Buffalo, NY, April 1-3, 2020

Source: nerccs2020.github.io

PhD Program in Network Science at CEU | Department of Network and Data Science

Complexity Digest - Mon, 11/11/2019 - 12:52

The PhD program in Network Science is a research-oriented program that provides the only PhD degree in this field in Europe. Network science provides essential tools to study complex systems including society online and offline, the economy or urban traffic. Accordingly, the program provides hands-on experience with large datasets characterizing those systems and the skills needed to analyze them. At the same time, network science is a rapidly developing new discipline with ample opportunities to do fundamental research. Within the PhD program there are possibilities to carry out research either in applied or in theoretical-methodological directions.

Source: networkdatascience.ceu.edu

Robotic Self-Replication

Complexity Digest - Sun, 11/03/2019 - 10:19

The concept of an artificial corporeal machine that can reproduce has attracted the attention of researchers from various fields over the past century. Some have approached the topic with a desire to understand biological life and develop artificial versions; others have examined it as a potentially practical way to use material resources from the moon and Mars to bootstrap the exploration and colonization of the solar system. This review considers both bodies of literature, with an emphasis on the underlying principles required to make self-replicating robotic systems from raw materials a reality. We then illustrate these principles with machines from our laboratory and others and discuss how advances in new manufacturing processes such as 3-D printing can have a synergistic effect in advancing the development of such systems.

 

Robotic Self-Replication
Annual Review of Control, Robotics, and Autonomous Systems

Vol. 3:- (Volume publication date May 2020)

Matthew S. Moses and Gregory S. Chirikjian

Source: www.annualreviews.org

Complexity, a podcast by SFI

Complexity Digest - Sat, 11/02/2019 - 16:50

Far-reaching conversations with a worldwide network of scientists and mathematicians, philosophers and artists developing new frameworks to explain our universe’s deepest mysteries. Join host Michael Garfield at the Santa Fe Institute each week to learn about your world and the people who have dedicated their lives to exploring its emergent order: their stories, research, and insights…

Source: complexity.simplecast.com

Drivers are blamed more than their automated cars when both make mistakes

Complexity Digest - Fri, 11/01/2019 - 16:47

When an automated car harms someone, who is blamed by those who hear about it? Here we asked human participants to consider hypothetical cases in which a pedestrian was killed by a car operated under shared control of a primary and a secondary driver and to indicate how blame should be allocated. We find that when only one driver makes an error, that driver is blamed more regardless of whether that driver is a machine or a human. However, when both drivers make errors in cases of human–machine shared-control vehicles, the blame attributed to the machine is reduced. This finding portends a public under-reaction to the malfunctioning artificial intelligence components of automated cars and therefore has a direct policy implication: allowing the de facto standards for shared-control vehicles to be established in courts by the jury system could fail to properly regulate the safety of those vehicles; instead, a top-down scheme (through federal laws) may be called for.

 

Drivers are blamed more than their automated cars when both make mistakes

Edmond Awad, Sydney Levine, Max Kleiman-Weiner, Sohan Dsouza, Joshua B. Tenenbaum, Azim Shariff, Jean-François Bonnefon & Iyad Rahwan
Nature Human Behaviour (2019)

Source: www.nature.com

Multiscale Modelling Tool: Mathematical modelling of collective behaviour without the maths

Complexity Digest - Thu, 10/31/2019 - 22:15

Collective behaviour is of fundamental importance in the life sciences, where it appears at levels of biological complexity from single cells to superorganisms, in demography and the social sciences, where it describes the behaviour of populations, and in the physical and engineering sciences, where it describes physical phenomena and can be used to design distributed systems. Reasoning about collective behaviour is inherently difficult, as the non-linear interactions between individuals give rise to complex emergent dynamics. Mathematical techniques have been developed to analyse systematically collective behaviour in such systems, yet these frequently require extensive formal training and technical ability to apply. Even for those with the requisite training and ability, analysis using these techniques can be laborious, time-consuming and error-prone. Together these difficulties raise a barrier-to-entry for practitioners wishing to analyse models of collective behaviour. However, rigorous modelling of collective behaviour is required to make progress in understanding and applying it. Here we present an accessible tool which aims to automate the process of modelling and analysing collective behaviour, as far as possible. We focus our attention on the general class of systems described by reaction kinetics, involving interactions between components that change state as a result, as these are easily understood and extracted from data by natural, physical and social scientists, and correspond to algorithms for component-level controllers in engineering applications. By providing simple automated access to advanced mathematical techniques from statistical physics, nonlinear dynamical systems analysis, and computational simulation, we hope to advance standards in modelling collective behaviour. At the same time, by providing expert users with access to the results of automated analyses, sophisticated investigations that could take significant effort are substantially facilitated. Our tool can be accessed online without installing software, uses a simple programmatic interface, and provides interactive graphical plots for users to develop understanding of their models.

 

Marshall JAR, Reina A, Bose T (2019) Multiscale Modelling Tool: Mathematical modelling of collective behaviour without the maths. PLoS ONE 14(9): e0222906. https://doi.org/10.1371/journal.pone.0222906

Source: journals.plos.org

2nd International School on Informatics and Dynamics in Complex Networks

Complexity Digest - Thu, 10/31/2019 - 06:28

The school is organized at the University of Catania, Italy, by the Department of Electrical Electronics and Computer Science and the Cometa Consortium, with the technical sponsorship of the Italian Society for Chaos and Complexity.
It consists of a series of lectures given by leading scientists in the field, aiming at providing a comprehensive treatment from background material to advanced results. The school is specially directed to PhD students and young researchers interested to the diverse aspects of the theory and applications of complex networks in science and engineering. The school aims at encouraging cross-disciplinary discussions between participants and speakers and start new joint researches.

 

2nd International School on Informatics and Dynamics in Complex Networks
University of Catania, Catania, Italy 10 -14 February 2020
Application Deadline: december 20th 2019

Source: isidcn.dieei.unict.it

Information Characteristics, Processes, and Mechanisms of Self-Organization Evolution

Complexity Digest - Wed, 10/30/2019 - 16:45

Self-organization is a general mechanism for the creation of new structural pattern of systems. A pattern, in essence, is a relationship, an architecture, a way of organizing, and a structure of order, which can only be explained by information activities. The characteristics of self-organization behavior, such as openness, nonlinearity, inner randomness, inner feedback, information network, and holographic construction, provide corresponding conditions and basis for the self-organizing evolution of the system from the aspects of environmental information function, maintenance and construction of the overall information framework of the system, and exploration of new information mode of the system. Based on the general process and mechanism of self-organization system evolution, its corresponding basic stages have the significance and value of information activities. Generally speaking, the process of system elements differentiating from the original system is the decoupling of information association between relevant elements and original systems. The convergence process of forming system elements is the initial exploration of forming a new information model; the nucleation process of some initial stabilization modes is the creation of information codons; the development of the system according to a particular pattern is ergodic construction of information feedback chain indicated by information codon; the diffusion of system self-replication is the expansion of the quantity of the information model; the variation in system self-replication is the innovation process of introducing new information pattern; environment-based selection and evolution correspond to the complex development of information pattern; and the alternation of old and new structures in system evolution corresponds to the formation process of the whole information network framework of the new system. In order to explain the self-organization’s characteristics, processes, and mechanisms of system evolution at a more comprehensive level, the complexity research program must pay enough attention to and give due status to the information factors and information science creed. Moreover, the information science research creed may also provide some basic theoretical paradigms with core theoretical significance for complex system research.

 

Information Characteristics, Processes, and Mechanisms of Self-Organization Evolution
Kun Wu and Qiong Nan

Complexity
Volume 2019, Article ID 5603685, 9 pages
https://doi.org/10.1155/2019/5603685

Source: www.hindawi.com

Sophisticated collective foraging with minimalist agents: a swarm robotics test

Complexity Digest - Mon, 10/28/2019 - 22:14

How groups of cooperative foragers can achieve efficient and robust collective foraging is of interest both to biologists studying social insects and engineers designing swarm robotics systems. Of particular interest are distance-quality trade-offs and swarm-size-dependent foraging strategies. Here, we present a collective foraging system based on virtual pheromones, tested in simulation and in swarms of up to 200 physical robots. Our individual agent controllers are highly simplified, as they are based on binary pheromone sensors. Despite being simple, our individual controllers are able to reproduce classical foraging experiments conducted with more capable real ants that sense pheromone concentration and follow its gradient. One key feature of our controllers is a control parameter which balances the trade-off between distance selectivity and quality selectivity of individual foragers. We construct an optimal foraging theory model that accounts for distance and quality of resources, as well as overcrowding, and predicts a swarm-size-dependent strategy. We test swarms implementing our controllers against our optimality model and find that, for moderate swarm sizes, they can be parameterised to approximate the optimal foraging strategy. This study demonstrates the sufficiency of simple individual agent rules to generate sophisticated collective foraging behaviour.

 

Sophisticated Collective Foraging with Minimalist Agents: A Swarm Robotics Test

M.S. Talamali, T. Bose, M. Haire, X. Xu, J.A.R. Marshall, A. Reina. Sophisticated Collective Foraging with Minimalist Agents: A Swarm Robotics Test. Swarm Intelligence 14(1):in press, 2020.
https://link.springer.com/article/10.1007/s11721-019-00176-9

Video: https://youtu.be/osQYuQ3cxmQ

Source: link.springer.com

Towards a quantitative model of epidemics during conflicts

Complexity Digest - Mon, 10/28/2019 - 15:47

Epidemics may contribute to and arise as a result of conflict. The effects of conflict on infectious diseases are complex. There have been counter-intuitive observations of both increase and decrease in disease outbreaks during and after conflicts. However there is no unified mathematical model that explains all these observations. There is an urgent need for a quantitative framework for modelling conflicts and epidemics. The article introduces a set of mathematical models to understand the role of conflicts in epidemics. The corresponding mathematical framework has the potential to explain the counter intuitive observations and the complex role of human conflicts in epidemics. This work suggests that aid and peacekeeping organizations should take an integrated approach that combines public health measures, socio-economic development, and peacekeeping in conflict zones.

This approach exemplifies the role of non-linear thinking in complex systems like human societies. The work presented should be looked upon as a first step towards a quantitative model of disease spread in conflicts.

 

Towards a quantitative model of epidemics during conflicts

Soumya Banerjee

INDECS

Source: indecs.eu

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